IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Imageprocessing

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
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Email: info@elysiumtechnologies.com

IEEE Project List 2011 - 2012

Abstract IMAGE PROCESSING
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2011 - 2012

01 1-D Transforms for the Motion Compensation Residual

Transforms used in image coding are also commonly used to compress prediction residuals in video coding. Prediction
residuals have different spatial characteristics from images, and it is useful to develop transforms that are adapted to
prediction residuals. In this paper, we explore the differences between the characteristics of images and motion
compensated prediction residuals by analyzing their local anisotropic characteristics and develop transforms adapted to
the local anisotropic characteristics of these residuals. The analysis indicates that many regions of motion compensated
prediction residuals have 1-D anisotropic characteristics and we propose to use 1-D directional transforms for these
regions. We present experimental results with one example set of such transforms within the H.264/AVC codec and the
results indicate that the proposed transforms can improve the compression efficiency of motion compensated prediction
residuals over conventional transforms

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02 A Filtering Approach to Edge Preserving MAP Estimation of Images

The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both
blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical
Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at
the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the
segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a
computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the
filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener
filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best
general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the
specific problem of demosaicing.

03 A Generalized Unsharp Masking Algorithm

Enhancement of contrast and sharpness of an image is required in many applications. Unsharp masking is a classical tool
for sharpness enhancement. We propose a generalized unsharp masking algorithm using the exploratory data model as a
unified framework. The proposed algorithm is designed to address three issues: 1) simultaneously enhancing contrast and
sharpness by means of individual treatment of the model component and the residual, 2) reducing the halo effect by means
of an edge-preserving filter, and 3) solving the out-of-range problem by means of log-ratio and tangent operations. We also
present a study of the properties of the log-ratio operations and reveal a new connection between the Bregman divergence
and the generalized linear systems. This connection not only provides a novel insight into the geometrical property of such
systems, but also opens a new pathway for system development. We present a new system called the tangent system
which is based upon a specific Bregman divergence. Experimental results, which are comparable to recently published
results, show that the proposed algorithm is able to significantly improve the contrast and sharpness of an image. In the
proposed algorithm, the user can adjust the two parameters controlling the contrast and sharpness to produce the desired

Madurai Trichy Kollam
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results. This makes the proposed algorithm practically useful.

04 A Geometric Method for Optimal Design of Color Filter Arrays

A color filter array (CFA) used in a digital camera is a mosaic of spectrally selective filters, which allows only one color
component to be sensed at each pixel. The missing two components of each pixel have to be estimated by methods known
as demosaicking. The demosaicking algorithm and the CFA design are crucial for the quality of the output images. In this
paper, we present a CFA design methodology in the frequency domain. The frequency structure, which is shown to be just
the symbolic DFT of the CFA pattern (one period of the CFA), is introduced to represent images sampled with any
rectangular CFAs in the frequency domain. Based on the frequency structure, the CFA design involves the solution of a
constrained optimization problem that aims at minimizing the demosaicking error. To decrease the number of parameters
and speed up the parameter searching, the optimization problem is reformulated as the selection of geometric points on the
boundary of a convex polygon or the surface of a convex polyhedron. Using our methodology, several new CFA patterns
are found, which outperform the currently commercialized and published ones. Experiments demonstrate the effectiveness
of our CFA design methodology and the superiority of our new CFA patterns.

05 A Hybrid Approach to Detect and Localize Texts in Natural Scene Images

Text detection and localization in natural scene images is important for content-based image analysis. This problem is
challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line
orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text
region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help
segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional
random field (CRF) model considering unary component properties and binary contextual component relationships with
supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-
based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring
manual tuning. Experimental results evaluated on the ICDAR2005 competition dataset showthat our approach yields higher
precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual
image dataset with promising results.

06 A Linear Programming Approach for Optimal Contrast-Tone Mapping

This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a
fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed
approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints
that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming.
This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the
constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new
approach over histogram equalization and its variants.


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07 A Majorize–Minimize Strategy for Subspace Optimization Applied to Image Restoration

This paper proposes accelerated subspace optimization methods in the context of image restoration. Subspace
optimization methods belong to the class of iterative descent algorithms for unconstrained optimization. At each iteration
of such methods, a stepsize vector allowing the best combination of several search directions is computed through a
multidimensional search. It is usually obtained by an inner iterative second-order method ruled by a stopping criterion that
guarantees the convergence of the outer algorithm. As an alternative, we propose an original multidimensional search
strategy based on the majorize–minimize principle. It leads to a closed-form stepsize formula that ensures the convergence
of the subspace algorithm whatever the number of inner iterations. The practical efficiency of the proposed scheme is
illustrated in the context of edge-preserving image restoration.

08 A Maximum Likelihood Approach to Joint Image Registration and Fusion

Both image registration and fusion can be formulated as estimation problems. Instead of estimating the registration
parameters and the true scene separately as in the conventional way, we propose a maximum likelihood approach for joint
image registration and fusion in this paper. More precisely, the fusion performance is used as the criteria to evaluate the
registration accuracy. Hence, the registration parameters can be automatically tuned so that both fusion and registration
can be optimized simultaneously. The expectation maximization algorithm is employed to solve this joint optimization
problem. The Cramer-Rao bound (CRB) is then derived. Our experiments use several types of sensory images for
performance evaluation, such as visual images, IR thermal images, and hyperspectral images. It is shown that the mean
square error of estimating the registration parameters using the proposed method is close to the CRBs. At the mean time,
an improved fusion performance can be achieved in terms of the edge preservation measure Q^AB/F , compared to the
Laplacian pyramid fusion approach.

09 A New Hybrid Method for Image Approximation Using the Easy Path Wavelet Transform

The easy path wavelet transform (EPWT) has recently been proposed by one of the authors as a tool for sparse
representations of bivariate functions from discrete data, in particular from image data. The EPWT is a locally adaptive
wavelet transform. It works along pathways through the array of function values and exploits the local correlations of the
given data in a simple appropriate manner. However, the EPWT suffers from its adaptivity costs that arise from the storage
of path vectors. In this paper, we propose a new hybrid method for image approximation that exploits the advantages of the
usual tensor product wavelet transform for the representation of smooth images and uses the EPWT for an efficient
representation of edges and texture. Numerical results show the efficiency of this procedure.

10 A Novel 3-D Color Histogram Equalization Method With Uniform 1-D Gray Scale Histogram


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The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color
histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale
histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform
distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test
results with natural and synthetic images are presented to compare and analyze various color histogram equalization
algorithms based upon 3-D color histograms.We also present theoretical analysis for nonideal performance of existing
methods.

11 A Stratified Approach for Camera Calibration Using Spheres

This paper proposes a stratified approach for camera calibration using spheres. Previous works have exploited epipolar
tangents to locate frontier points on spheres for estimating the epipolar geometry. It is shown in this paper that other than
the frontier points, two additional point features can be obtained by considering the bitangent envelopes of a pair of
spheres. A simple method for locating the images of such point features and the sphere centers is presented. An algorithm
for recovering the fundamental matrix in a plane plus parallax representation using these recovered image points and the
epipolar tangents from three spheres is developed. A new formulation of the absolute dual quadric as a cone tangent to a
dual sphere with the plane at infinity being its vertex is derived. This allows the recovery of the absolute dual quadric,
which is used to upgrade the weak calibration to a full calibration. Experimental results on both synthetic and real data are
presented, which demonstrate the feasibility and the high precision achieved by our proposed algorithm.

12 A Uniform Framework for Estimating Illumination Chromaticity, Correspondence, and Specular Reflection

Based upon a new correspondence matching invariant called illumination chromaticity constancy, we present a new
solution for illumination chromaticity estimation, correspondence searching, and specularity removal. Using as few as two
images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity
hypotheses via correspondence matching. The hypothesis with the highest vote is accepted as correct. The estimated
illumination chromaticity is then used together with the new matching invariant to match highlights, which inherently
provides solutions for correspondence searching and specularity removal. Our method differs from the previous
approaches: those treat these vision problems separately and generally require that specular highlights be detected in a
preprocessing step. Also, our method uses more images than previous illumination chromaticity estimation methods,
which increases its robustness because more inputs/constraints are used. Experimental results on both synthetic and real
images demonstrate the effectiveness of the proposed method.

13 A Variational Model for Histogram Transfer of Color Images

In this paper, we propose a variational formulation for histogram transfer of two or more color images. We study an energy
functional composed by three terms: one tends to approach the cumulative histograms of the transformed images, the
other two tend to maintain the colors and geometry of the original images. By minimizing this energy, we obtain an
algorithm that balances equalization and the conservation of features of the original images. As a result, they evolve while
approaching an intermediate histogram between them. This intermediate histogram does not need to be specified in
advance, but it is a natural result of the model. Finally, we provide experiments showing that the proposed method


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compares well with the state of the art.

14 A Variational Model for Segmentation of Overlapping Objects With Additive Intensity Value

We propose a variant of the Mumford–Shah model for the segmentation of a pair of overlapping objects with additive
intensity value. Unlike standard segmentation models, it does not only determine distinct objects in the image, but also
recover the possibly multiple membership of the pixels. To accomplish this, some a priori knowledge about the
smoothness of the object boundary is integrated into the model. Additivity is imposed through a soft constraint which
allows the user to control the degree of additivity and is more robust than the hard constraint. We also show analytically
that the additivity parameter can be chosen to achieve some stability conditions. To solve the optimization problem
involving geometric quantities efficiently, we apply a multiphase level set method. Segmentation results on synthetic and
real images validate the good performance of our model, and demonstrate the model’s applicability to images with multiple
channels and multiple objects.

15 Accelerating X-Ray Data Collection Using Pyramid Beam Ray Casting Geometries

Image reconstruction from its projections is a necessity in many applications such as medical (CT), security, inspection,
and others. This paper extends the 2-D Fan-beam method in [2] to 3-D. The algorithm, called Pyramid Beam (PB), is based
upon the parallel reconstruction algorithm in [1]. It allows fast capturing of the scanned data, and in 3-D, the
reconstructions are based upon the discrete X-ray transform [1]. The PB geometries are reordered to fit parallel projection
geometry. The underlying idea is to use the algorithm in [1] by porting the proposed PB geometries to fit the algorithm in
[1]. The complexity of the algorithm is comparable with the 3-D FFT. The results show excellent reconstruction qualities
while being simple for practical use.

16 Adaptive Multiwavelet-Based Watermarking Through JPW Masking

In this paper, a multibit, multiplicative, spread spectrum watermarking using the discrete multiwavelet (including
unbalanced and balanced multiwavelet) transform is presented. Performance improvement with respect to existing
algorithm is obtained by means of a new just perceptual weighting (JPW) model. The new model incorporates various
masking effects of human visual perception by taking into account the eye’s sensitivity to noise changes depending on
spatial frequency, luminance and texture of all the image subbands. In contrast to conventional JND threshold model, JPW
describing minimum perceptual sensitivity weighting to noise changes, is fitter for nonadditive watermarking. Specifically,
watermarking strength is adaptively adjusted to obtain minimum perceptual distortion by employing the JPW model.
Correspondingly, an adaptive optimum decoding is derived using a statistic model based on generalized-Gaussian
distribution (GGD) for multiwavelet coefficients of the cover-image. Furthermore, the impact of multiwavelet characteristics
on proposed watermarking scheme is also analyzed. Finally, the experimental results show that proposed JPW model can
improve the quality of the watermarked image and give more robustness of the watermark as compared with a variety of
state-of-the-art algorithms.


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17 Adaptive Sequential Prediction of Multidimensional Signals With Applications to Lossless Image Coding

We investigate the problem of designing adaptive sequential linear predictors for the class of piecewise autoregressive
multidimensional signals, and adopt an approach of minimum description length (MDL) to determine the order of the
predictor and the support on which the predictor operates. The design objective is to strike a balance between the bias and
variance of the prediction errors in the MDL criterion. The predictor design problem is particularly interesting and
challenging for multidimensional signals (e.g., images and videos) because of the increased degree of freedom in choosing
the predictor support. Our main result is a new technique of sequentializing a multidimensional signal into a sequence of
nested contexts of increasing order to facilitate the MDL search for the order and the support shape of the predictor, and
the sequentialization is made adaptive on a sample by sample basis. The proposed MDL-based adaptive predictor is applied
to lossless image coding, and its performance is empirically established to be the best among all the results that have been
published till present.

18 An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP),
where a (possibly nonsmooth) regularizer is minimized under the constraint that the solution explains the observations
sufficiently well. Although the regularizer and constraint are usually convex, several particular features of these problems
(huge dimensionality, nonsmoothness) preclude the use of off-the-shelf optimization tools and have stimulated a
considerable amount of research. In this paper, we propose a new efficient algorithm to handle one class of constrained
problems (often known as basis pursuit denoising) tailored to image recovery applications. The proposed algorithm, which
belongs to the family of augmented Lagrangian methods, can be used to deal with a variety of imaging IPLIP, including
deconvolution and reconstruction from compressive observations (such as MRI), using either total-variation or wavelet-
based (or, more generally, frame-based) regularization. The proposed algorithm is an instance of the so-called alternating
direction method of multipliers, for which convergence sufficient conditions are known; we show that these conditions are
satisfied by the proposed algorithm. Experiments on a set of image restoration and reconstruction benchmark problems
show that the proposed algorithm is a strong contender for the state-of-the-art.

19 An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets

Geometric wavelet is a recent development in the field of multivariate nonlinear piecewise polynomials approximation. The
present study improves the geometric wavelet (GW) image coding method by using the slope intercept representation of
the straight line in the binary space partition scheme. The performance of the proposed algorithm is compared with the
wavelet transform-based compression methods such as the embedded zerotree wavelet (EZW), the set partitioning in
hierarchical trees (SPIHT) and the embedded block coding with optimized truncation (EBCOT), and other recently


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developed “sparse geometric representation” based compression algorithms. The proposed image compression algorithm
outperforms the EZW, the Bandelets and the GW algorithm. The presented algorithm reports a gain of 0.22 dB over the GW
method at the compression ratio of 64 for the Cameraman test image.

20 An Iterative Shrinkage Approach to Total-Variation Image Restoration

The problem of restoration of digital images from their degraded measurements plays a central role in a multitude of
practically important applications. A particularly challenging instance of this problem occurs in the case when the
degradation phenomenon is modeled by an ill-conditioned operator. In such a situation, the presence of noise makes it
impossible to recover a valuable approximation of the image of interest without using some a priori information about its
properties. Such a priori information—commonly referred to as simply priors—is essential for image restoration, rendering
it stable and robust to noise. Moreover, using the priors makes the recovered images exhibit some plausible features of
their original counterpart. Particularly, if the original image is known to be a piecewise smooth function, one of the standard
priors used in this case is defined by the Rudin-Osher-Fatemi model, which results in total variation (TV) based image
restoration. The current arsenal of algorithms for TV-based image restoration is vast. In this present paper, a different
approach to the solution of the problem is proposed based upon the method of iterative shrinkage (aka iterated
thresholding). In the proposed method, the TV-based image restoration is performed through a recursive application of two
simple procedures, viz. linear filtering and soft thresholding. Therefore, the method can be identified as belonging to the
group of first-order algorithms which are efficient in dealing with images of relatively large sizes. Another valuable feature
of the proposed method consists in its working directly with the TV functional, rather then with its smoothed versions.
Moreover, the method provides a single solution for both isotropic and anisotropic definitions of the TV functional, thereby
establishing a useful connection between the two formulae. Finally, a number of standard examples of image deblurring are
demonstrated, in which the proposed method can provide restoration results of superior quality as compared to the case of
sparse-wavelet deconvolution.

21 An Optimal Data Hiding Scheme With Tree-Based Parity Check

Reducing distortion between the cover object and the stego object is an important issue for steganography. The tree-based
parity check method is very efficient for hiding a message on image data due to its simplicity. Based on this approach, we
propose a majority vote strategy that results in least distortion for finding a stego object. The lower embedding efficiency of
our method is better than that of previous works when the hidden message length is relatively large.

22 An Orientation Inference Framework for Surface Reconstruction From Unorganized Point Clouds

In this paper, we present an orientation inference framework for reconstructing implicit surfaces from unoriented point
clouds. The proposed method starts from building a surface approximation hierarchy comprising of a set of unoriented
local surfaces, which are represented as a weighted combination of radial basis functions. We formulate the determination
of the globally consistent orientation as a graph optimization problem by treating the local implicit patches as nodes. An


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energy function is defined to penalize inconsistent orientation changes by checking the sign consistency between
neighboring local surfaces. An optimal labeling of the graph nodes indicating the orientation of each local surface can,
thus, be obtained by minimizing the total energy defined on the graph. The local inference results are propagated over the
model in a front-propagation fashion to obtain the global solution. The reconstructed surfaces are consolidated by a simple
and effective inspection procedure to locate the erroneously fitted local surfaces. A progressive reconstruction algorithm
that iteratively includes more oriented points to improve the fitting accuracy and efficiently updates the RBF coefficients is
proposed. We demonstrate the performance of the proposed method by showing the surface reconstruction results on
some real-world 3-D data sets with comparison to those by using the previous methods.

23 Anisotropic Morphological Filters With Spatially-Variant Structuring Elements Based on Image-Dependent Gradient Fields

This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and
formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using
exclusively the structuring function, without resorting to complement. This theoretical framework allows to build
morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant
direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted
from the image under study: the orientation is given by means of a diffusion process of the average square gradient field,
which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the
image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant
edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic
features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based
filters prove the validity of this theoretical sound and novel approach.

24 Autofluorescence Removal by Non-Negative Matrix Factorization

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF)
from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the
concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded
by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify
how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources.
Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do
not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-
negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing,
but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent
images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on
fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous
state of the art on validation images.

25 Automatic Exact Histogram Specification for Contrast Enhancement and Visual System Based Quantitative Evaluation


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Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast
enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for
global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image
histogram to a modification process and then by maximizing a measure that represents increase in information and
decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-
surround retinal receptive field model is also devised in this paper. This method works at multiple scales (frequency bands)
and combines the contrast measures obtained at different scales using -norm. In comparison to a few existing methods,
the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is
demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

26 Balanced Multifilter Banks for Multiple Description Coding

The parametrization for one kind of multifilter banks generating balanced multiwavelets is presented in this paper, in which
two lowpass filters are flipping filters, and two highpass filters have linear phase. Based on these parametric expressions,
some balanced multiwavelets and analysis-ready multiwavelets are constructed, which are symmetric, or antisymmetric.
Moreover, on the basis of balanced multiwavelet transform, a new method of multiple description coding is given, and
experiments show that this method works well. Compared with the traditional multiple description coding method, this
method has low redundancy.

27 Balanced Multiwavelets With Interpolatory Property

Balanced multiwavelets with interpolatory property are discussed in this paper. This kind of multiwavelets can have a
sampling property like Shannon’s sampling theorem. It has been shown that the corresponding matrix-valued refinable
mask has special structure, and an orthogonal multifilter bank{H(z),G(Z)} can be reduced to a scalar valued conjugate
quadrature filter (CQF) a(z). But it does not mean that any scalar CQF can form a “good” multifilter bank which can generate
a vector-valued refinable function with some degree of smoothness. In the context of balanced multiwavelets, we give the
definition of transferring balance order, which a scalar CQF a(z) satisfies, to guarantee that the multiwavelet generated is
balanced. On the basis of the parametrization of a scalar CQF with any length and conditions of transferring balance order,
parametrization of multifilter banks which can generate interpolatory multiwavelet and interpolatory scaling function, is
gotten. Moreover, some balanced interpolatory multiwavelets have been constructed. Interpolatory analysis-ready
multiwavelets (armlets) are also discussed in this paper. It is known that conditions of armlets are easy to validate,
compared with balanced multiwavelets. But it will be present that if the corresponding scaling function is interpolatory, the
multiwavelet is balanced of order if and only if it is an armlet of order . Finally, the application of balanced multiwavelets
with interpolatory property in image processing is also discussed.

28 Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation

In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown
image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV)
method, the regularization parameters associated with the unknown image and blur can be updated in alternating


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minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational
Bayesian blind deconvolution algorithms with Student’s-t priors or a total variation prior.

29 Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining
the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that
the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a
unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the
abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint
any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed
in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF
algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and
the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index
assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the
preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic
mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the
proposed method.

30 Boosting Color Feature Selection for Color Face Recognition

This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-
component feature selection framework. The proposed boosting color-component feature selection framework is designed
for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR
performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component
features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The
effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs):
CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed
method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges
including highly uncontrolled illumination, moderate pose variation, and small resolution face images.

31 Characterization of Electrophotographic Print Artifacts: Banding, Jitter, and Ghosting

Electrophotographic (EP) print banding, jitter, and ghosting artifacts are common sources of print quality degradation.
Traditionally, the characterization of banding and jitter artifacts relies mainly on the assumption that the defect has either a
horizontal or vertical orientation which permits the simple 1-D analysis of the defect profile. However, this assumption can
easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases, the
defect can inherently be neither vertical nor horizontal. In this case, unless the defect orientation has been accurately
detected before analysis, the 1-D-based approaches could bias the estimation of the defect severity. In this paper, we
present an approach to characterize the jitter and banding artifacts of unrestricted orientation using wavelet filtering and 2-
D spectral analysis. We also present a new system for detecting and quantifying ghosting defects. It includes a design for a
printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis.Wavelet filtering and a


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template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric
is developed to quantify ghosting based upon its contrast, shape, and location consistency. Our experimental results show
that the proposed approaches provide objective measures that quantify EP defects with a rank ordering correlation
coefficient of 0.8 to 0.98, as compared to the subjective assessment of print quality experts.

32 Classification-Based Adaptive Filtering for Multiframe Blind Image Restoration

In this paper, the blind restoration of a scene is investigated, when multiple degraded (blurred and noisy) acquisitions are
available. An adaptive filtering technique is proposed, where the distorted images are filtered, classified and then fused
based upon the classification decisions. Finite normal-density mixture (FNM) models are used to model the filtered outputs
at each iteration. For simplicity, fixed number of Gaussian components (classes) is, initially, considered for each degraded
frame and the selection of the optimal number of classes is performed according to the global relative entropy criterion.
However, there exist cases where dynamically varyingFNMmodels should be considered, where the optimal number of
classes is selected according to the Akaike information criterion. The iterative application of classification and fusion,
followed by optimal adaptive filtering, converges to a global enhanced representation of the original scene in only a few
iterations. The proposed restoration method does not require knowledge of the point-spread-function support size or exact
alignment of the acquired frames. Simulation results on synthetic and real data, using both fixed and dynamically varying
FNM models, demonstrate its efficiency under both noisy and noise-free conditions.

33 Color Extended Visual Cryptography Using Error Diffusion

Color visual cryptography (VC) encrypts a color secret message into color halftone image shares. Previous methods in
the literature show good results for black and white or gray scale VC schemes, however, they are not sufficient to be
applied directly to color shares due to different color structures. Some methods for color visual cryptography are not
satisfactory in terms of producing either meaningless shares or meaningful shares with low visual quality, leading to
suspicion of encryption. This paper introduces the concept of visual information pixel (VIP) synchronization and error
diffusion to attain a color visual cryptography encryption method that produces meaningful color shares with high visual
quality. VIP synchronization retains the positions of pixels carrying visual information of original images throughout the
color channels and error diffusion generates shares pleasant to human eyes. Comparisons with previous approaches show
the superior performance of the new method.

34 Combined Invariants to Similarity Transformation and to Blur Using Orthogonal Zernike Moments

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set
of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to
convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical
framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur
invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The
comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy,
template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the
overall better.


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35 Comments on “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering”

In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40,
[1] proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary,
and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification
approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both
peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.

36 Compressibility-Aware Media Retargeting With Structure Preserving

A number of algorithms have been proposed for intelligent image/video retargeting with image content retained as much as
possible. However, they usually suffer from some artifacts in the results, such as ridge or structure twist. In this paper, we
present a structure-preserving media retargeting technique that preserves the content and image structure as best as
possible. Different from the previous pixel or grid based methods, we estimate the image content saliency from the
structure of the content. A block structure energy is introduced with a top-down strategy to constrain the image structure
inside to deform uniformly in either or direction. However, the flexibilities for retargeting are quite different for different
images. To cope with this problem, we propose a compressibility assessment scheme for media retargeting by combining
the entropies of image gradient magnitude and orientation distributions. Thus, the resized media is produced to preserve
the image content and structure as best as possible. Our experiments demonstrate that the proposed method provides
resized images/ videos with better preservation of content and structure than those by the previous methods

37 Computational Perceptual Features for Texture Representation and Retrieval

A perception-based approach to content-based image representation and retrieval is proposed in this paper.We consider
textured images and propose to model their textural content by a set of features having a perceptual meaning and their
application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features,
namely coarseness, directionality, contrast, and busyness. The proposed computational measures can be based upon two
representations: the original images representation and the autocorrelation function (associated with original images)
representation. The set of computational measures proposed is applied to content-based image retrieval on a large image
data set, the well-known Brodatz database. Experimental results and benchmarking show interesting performance of our
approach. First, the correspondence of the proposed computational measures to human judgments is shown using a
psychometric method based upon the Spearman rank-correlation coefficient. Second, the application of the proposed
computational measures in texture retrieval shows interesting results, especially when using results fusion returned by
each of the two representations. Comparison is also given with related works and show excellent performance of our
approach compared to related approaches on both sides: correspondence of the proposed computational measures with
human judgments as well as the retrieval effectiveness.


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38 Constrained Acquisition of Ink Spreading Curves From Printed Color Images

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set
of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to
convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical
framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur
invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The
comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy,
template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the
overall better.

39 Contactless and Pose Invariant Biometric Identification Using Hand Surface

This paper presents a novel approach for hand matching that achieves significantly improved performance even in the
presence of large hand pose variations. The proposed method utilizes a 3-D digitizer to simultaneously acquire intensity
and range images of the user’s hand presented to the system in an arbitrary pose. The approach involves determination of
the orientation of the hand in 3-D space followed by pose normalization of the acquired 3-D and 2-D hand images.
Multimodal (2-D as well as 3-D) palmprint and hand geometry features, which are simultaneously extracted from the user’s
pose normalized textured 3-D hand, are used for matching. Individual matching scores are then combined using a new
dynamic fusion strategy. Our experimental results on the database of 114 subjects with significant pose variations yielded
encouraging results. Consistent (across various hand features considered) performance improvement achieved with the
pose correction demonstrates the usefulness of the proposed approach for hand based biometric systems with
unconstrained and contact-free imaging. The experimental results also suggest that the dynamic fusion approach
employed in this work helps to achieve performance improvement of 60% (in terms of EER) over the case when matching
scores are combined using the weighted sum rule.

40 Contextual Kernel and Spectral Methods for Learning the Semantics of Images

This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to
automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic
image annotation, we define a novel spatial string kernel to quantify the similarity between images. Specifically, we
represent each image as a 2-D sequence of visual words and measure the similarity between two 2-D sequences using the
shared occurrences of -length 1-D subsequences by decomposing each 2-D sequence into two orthogonal 1-D sequences.
Based on our proposed spatial string kernel, we further formulate automatic image annotation as a contextual keyword
propagation problem, which can be solved very efficiently by linear programming. Unlike the traditional relevance models
that treat each keyword independently, the proposed contextual kernel method for keyword propagation takes into account
the semantic context of annotation keywords and propagates multiple keywords simultaneously. Significantly, this type of


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semantic context can also be incorporated into spectral embedding for refining the annotations of images predicted by
keyword propagation. Experiments on three standard image datasets demonstrate that our contextual kernel and spectral
methods can achieve significantly better results than the state of the art.

41 Contextual Object Localization With Multiple Kernel Nearest Neighbor

Recently, many object localization models have shown that incorporating contextual cues can greatly improve accuracy
over using appearance features alone. Therefore, many of these models have explored different types of contextual
sources, but only considering one level of contextual interaction at the time. Thus, what context could truly contribute to
object localization, through integrating cues from all levels, simultaneously, remains an open question. Moreover, the
relative importance of the different contextual levels and appearance features across different object classes remains to be
explored. Here we introduce a novel framework for multiple class object localization that incorporates different levels of
contextual interactions. We study contextual interactions at the pixel, region and object level based upon three different
sources of context: semantic, boundary support, and contextual neighborhoods. Our framework learns a single similarity
metric from multiple kernels, combining pixel and region interactions with appearance features, and then applies a
conditional random field to incorporate object level interactions. To effectively integrate different types of feature
descriptions, we extend the large margin nearest neighbor to a novel algorithm that supports multiple kernels. We perform
experiments on three challenging image databases: Graz-02, MSRC and PASCAL VOC 2007. Experimental results show that
our model outperforms current state-of-the-art contextual frameworks and reveals individual contributions for each
contextual interaction level as well as appearance features, indicating their relative importance for object localization.

42 Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes
it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of
fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics
amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more
damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is
proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are
organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the
fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering
different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately
tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the
application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.

43 Dealing With Parallax in Shape-From-Focus

We propose a new method that extends the capability of shape-from-focus (SFF) to estimate the depth profile of 3-D objects
in the presence of structure-dependent pixel motion. Existing SFF techniques work under the constraint that there is no
parallax in the captured stack of frames. However, in off-the-shelf cameras, there can be appreciable pixel motion among
the observations when there is relative motion between the object and the camera. In such a scenario, the depth estimates


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will be erroneous if the parallax effect is not factored in. Our degradation model accounts for pixel migration effects in the
observations due to parallax resulting in a generalization of the SFF technique. We show that pixel motion and defocus blur
therein are tightly coupled to the underlying shape of the 3-D object. Simultaneous reconstruction of the underlying 3-D
structure and the all-in-focus image is carried out within an optimization framework using local image operations. The
proposed method when tested on many examples, both synthetic and real, is very effective and delivers state-of-the-art
performance.

44 Dictionary Learning for Stereo Image Representation

One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of
the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently
approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However,
the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete
dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model
where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo
views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry
constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization
algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a
parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo
omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits
of dictionary learning for distributed scene representation and camera pose estimation.

45 Diffuse Prior Monotonic Likelihood Ratio Test for Evaluation of Fused Image Quality Measures

This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the
effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human
perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The method computes a
new diffuse prior monotonic likelihood ratio (DPMLR) to facilitate the comparison of the H1 hypothesis that the intrinsic
human detection performance is related to the FIQM via a monotonic function against the null hypothesis that the detection
and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and
demonstrates the effectiveness of the DPMLR test via Monte Carlo simulations. Finally, the DPMLR is used to score FIQMs
with test cases considering over 35 scenes and various image fusion algorithms.

46 Direct Intermode Selection for H.264 Video Coding Using Phase Correlation

The H.264 video coding standard exhibits higher performance compared to the other existing standards such as H.263,
MPEG-X. This improved performance is achieved mainly due to the multiple-mode motion estimation and compensation.
Recent research tried to reduce the computational time using the predictive motion estimation, early zero motion vector
detection, fast motion estimation, and fast mode decision, etc. These approaches reduce the computational time
substantially, at the expense of degrading image quality and/or increase bitrates to a certain extent. In this paper, we use
phase correlation to capture the motion information between the current and reference blocks and then devise an algorithm


15



for direct motion estimation mode prediction, without excessive motion estimation. A bigger amount of computational time
is reduced by the direct mode decision and exploitation of available motion vector information from phase correlation. The
experimental results show that the proposed scheme outperforms the existing relevant fast algorithms, in terms of both
operating efficiency and video coding quality. To be more specific, 82~92%of encoding time is saved compared to the
exhaustive mode selection (against 58~74% in the relevant state-of-the-art), and this is achieved without jeopardizing
image quality (in fact, there is some improvement over the exhaustive mode selection at mid to high bit rates) and for a
wide range of videos and bitrates (another advantages over the relevant state-of-the-art).

47
Noise
Discretization Error Analysis and Adaptive Meshing Algorithms for Fluorescence Diffuse Optical Tomography in the Presence of Measurement

Quantitatively accurate fluorescence diffuse optical tomographic (FDOT) image reconstruction is a computationally
demanding problem that requires repeated numerical solutions of two coupled partial differential equations and an
associated inverse problem. Recently, adaptive finite element methods have been explored to reduce the computation
requirements of the FDOT image reconstruction. However, existing approaches ignore the ubiquitous presence of noise in
boundary measurements. In this paper, we analyze the effect of finite element discretization on the FDOT forward and
inverse problems in the presence of measurement noise and develop novel adaptive meshing algorithms for FDOT that take
into account noise statistics. We formulate the FDOT inverse problem as an optimization problem in the maximum a
posteriori framework to estimate the fluorophore concentration in a bounded domain. We use the mean-square-error (MSE)
between the exact solution and the discretized solution as a figure of merit to evaluate the image reconstruction accuracy,
and derive an upper bound on the MSE which depends upon the forward and inverse problem discretization parameters,
noise statistics, a priori information of fluorophore concentration, source and detector geometry, as well as background
optical properties. Next, we use this error bound to develop adaptive meshing algorithms for the FDOT forward and inverse
problems to reduce the MSE due to discretization in the reconstructed images. Finally, we present a set of numerical
simulations to illustrate the practical advantages of our adaptive meshing algorithms for FDOT image reconstruction.

48 Distributed Multiple Description Video Coding on Packet Loss Channels

In this paper, we are to solve the drift problem of multiple description video coding on packet loss channels by using state-
of-the-art distributed techniques. We first present an asymptotically optimal code design of multiple descriptions in the
Wyner–Ziv (MDWZ) setting. Then we propose a distributed multiple description video coding (DMDVC) scheme, which
performs MDWZ coding on each nonintra coded frame. Instead of the prediction loops used in traditional multiple
description video coding, Slepian–Wolf based coding is used to exploit interframe correlations. A bitplane extraction
scheme is proposed to improve the balance between two descriptions, so that side informations can be interchanged
between the side decoders ofDMDVCwith negligible quality degradation, which is crucial to robust transmission over
packet loss channels. Experiment results demonstrate the robustness of our scheme, especially at high packet loss rates.

49 Efficiently Learning a Detection Cascade With Sparse Eigenvectors

Real-time object detection has many computer vision applications. Since Viola and Jones [1] proposed the first real-time
AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we
first show that feature selection methods other than boosting can also be used for training an efficient object detector. In


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particular, we introduce greedy sparse linear discriminant analysis (GSLDA) [2] for its conceptual simplicity and
computational efficiency; and slightly better detection performance is achieved compared with [1]. Moreover, we propose a
new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train a detection
cascade. BGSLDA exploits the sample reweighting property of boosting and the class-separability criterion of GSLDA.
Experiments in the domain of highly skewed data distributions (e.g., face detection) demonstrate that classifiers trained
with the proposed BGSLDAoutperforms AdaBoost and its variants. This finding provides a significant opportunity to argue
that AdaBoost and similar approaches are not the only methods that can achieve high detection results for real-time object
detection.

50 Elastic Sequence Correlation for Human Action Analysis

This paper addresses the problem of automatically analyzing and understanding human actions from video footage. An
“action correlation” framework, elastic sequence correlation (ESC), is proposed to identify action subsequences from a
database of (possibly long) video sequences that are similar to a given query video action clip. In particular, we show that
two well-known algorithms, namely approximate pattern matching in computer and information sciences and dynamic time
warping (DTW) method in signal processing, are special cases of our ESC framework. The proposed framework is applied
to two important real-world applications: action pattern retrieval, as well as action segmentation and recognition, where, on
average, its run time speed (in matlab) is about 3.3 frames per second. In addition, comparing with the state-of-the-art
algorithms on a number of challenging data sets, our approach is demonstrated to perform competitively.

51 Enhanced Shift and Scale Tolerance for Rotation Invariant Polar Matching With Dual-Tree Wavelets

Polar matching is a recently developed shift and rotation invariant object detection method that is based upon dual-tree
complex wavelet transforms or equivalent multiscale directional filterbanks. It can be used to facilitate both keypoint
matching, neighborhood search detection, or detection and tracking with particle filters. The theory is extended here to
incorporate an allowance for local spatial and dilation perturbations.With experiments, we demonstrate that the robustness
of the polar matching method is strengthened at modest computational cost.

52 Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination
variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a
hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is
transformed into an illumination-insensitive image, called an “integral normalized gradient image,” by normalizing and
integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple
face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different
Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant
analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances.
Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is
applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated;
FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed
method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as


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illumination changes, expression changes, and time elapses.

53 Face Recognition by Exploring Information Jointly in Space, Scale and Orientation

Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in
either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to
have an important role in visual perception. This paper proposes a novel face representation and recognition approach by
exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first
decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters.
Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in
different scale and orientation responses. This way, information from different domains is explored to give a good face
representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection
or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET,
AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.

54 Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have
shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts.
However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical
applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has
been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a
fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NHICD) optimization. The NH-
ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a
mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in
greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon
the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed
up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper
bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally
expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of
various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a
factor of three on average for typical 3-D multislice geometries.

55 FAST Rate Allocation Through Steepest Descent for JPEG2000 Video Transmission


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This work addresses the transmission of pre-encoded JPEG2000 video within a video-on-demand scenario. The primary
requirement for the rate allocation algorithm deployed in the server is to match the real-time processing demands of the
application. Scalability in terms of complexity must be provided to supply a valid solution by a given instant of time. The
FAst rate allocation through STeepest descent (FAST) method introduced in this work selects an initial (and possibly poor)
solution, and iteratively improves it until time is exhausted or the algorithm finishes execution. Experimental results
suggest that FAST commonly achieves solutions close to the global optimum while employing very few computational
resources.

56 Fast Sparse Image Reconstruction Using Adaptive Nonlinear Filtering

Compressed sensing is a new paradigm for signal recovery and sampling. It states that a relatively small number of linear
measurements of a sparse signal can contain most of its salient information and that the signal can be exactly
reconstructed from these highly incomplete observations. The major challenge in practical applications of compressed
sensing consists in providing efficient, stable and fast recovery algorithms which, in a few seconds, evaluate a good
approximation of a compressible image from highly incomplete and noisy samples. In this paper, we propose to approach
the compressed sensing image recovery problem using adaptive nonlinear filtering strategies in an iterative framework, and
we prove the convergence of the resulting two-steps iterative scheme. The results of several numerical experiments
confirm that the corresponding algorithm possesses the required properties of efficiency, stability and low computational
cost and that its performance is competitive with those of the state of the art algorithms.

57 Fine-Granularity and Spatially-Adaptive Regularization for Projection-Based Image Deblurring

This paper studies two classes of regularization strategies to achieve an improved tradeoff between image recovery and
noise suppression in projection-based image deblurring. The first is based on a simple fact that -times Landweber iteration
leads to a fixed level of regularization, which allows us to achieve fine-granularity control of projection-based iterative
deblurring by varying the value . The regularization behavior is explained by using the theory of Lagrangian multiplier for
variational schemes. The second class of regularization strategy is based on the observation that various regularized filters
can be viewed as nonexpansive mappings in the metric space. A deeper understanding about different regularization filters
can be gained by probing into their asymptotic behavior—the fixed point of nonexpansive mappings. By making an analogy
to the states of matter in statistical physics, we can observe that different image structures (smooth regions, regular edges
and textures) correspond to different fixed points of nonexpansive mappings when the temperature(regularization)
parameter varies. Such an analogy motivates us to propose a deterministic annealing based approach toward spatial
adaptation in projection-based image deblurring. Significant performance improvements over the current state-of-the-art
schemes have been observed in our experiments, which substantiates the effectiveness of the proposed regularization
strategies.

58 Fractal Dimension of Color Fractal Images

Fractal dimension is a very useful metric for the analysis of the images with self-similar content, such as textures. For its
computation there exist several approaches, the probabilistic algorithm being accepted as the most elegant approach.


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However, all the existing methods are defined for 1-D signals or binary images, with extension to grayscale images. Our
purpose is to propose a color version of the probabilistic algorithm for the computation of the fractal dimension. To validate
this new approach, we also propose an extension of the existing algorithm for the generation of probabilistic fractals, in
order to obtain color fractal images. Then we show the results of our experiments and conclude this paper.

59 From Local Pixel Structure to Global Image Super-Resolution: A New Face Hallucination Framework

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution
(LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new
framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution
(HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned
pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the
framework. Step 1 searches the database for K example faces that are the most similar to the input, and then warps the K
example images to the input using optical flow. Step 2 uses the warped HR version of the K example faces to learn the
local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face,
and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping
errors, have been developed. Step 3 estimates the targetHRface by solving a constrained optimization problem by means of
an iterative procedure. Experimental results show that our new method can provide good performances for face
hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art
methods.

60 From Point to Local Neighborhood: Polyp Detection in CT Colonography Using Geodesic Ring Neighborhoods

Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. These
assume that the discrete triangulated surface mesh or volume closely approximates a smooth continuous surface.
However, this is often not the case and because curvature is computed as a local feature and a second-order differential
quantity, the presence of noise significantly affects its estimation. For this reason, a more global feature is required to
provide an accurate description of the surface at hand. In this paper, a novel method incorporating a local neighborhood
around the centroid of a surface patch is proposed. This is done using geodesic rings which accumulate curvature
information in a neighborhood around this centroid. This geodesic-ring neighborhood approximates a single smooth,
continuous surface upon which curvature and orientation estimation methods can be applied. A new global shape index, S
is also introduced and computed. These curvature and orientation values will be used to classify the surface as either a
bulbous polyp, ridge-like fold or semiplanar structure. Experimental results show that this method is promising (100%
sensitivity, 100% specificity for lesions > 10 mm) for distinguishing between bulbous polyps, folds and planar-like
structures in the colon.

61 From Tiger to Panda: Animal Head Detection

Robust object detection has many important applications in real-world online photo processing. For example, both Google
image search and MSN live image search have integrated human face detector to retrieve face or portrait photos. Inspired
by the success of such face filtering approach, in this paper, we focus on another popular online photo category—animal,
which is one of the top five categories in the MSN live image search query log. As a first attempt, we focus on the problem


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of animal head detection of a set of relatively large land animals that are popular on the internet, such as cat, tiger, panda,
fox, and cheetah. First, we proposed a new set of gradient oriented feature, Haar of Oriented Gradients (HOOG), to
effectively capture the shape and texture features on animal head. Then, we proposed two detection algorithms, namely
Bruteforce detection and Deformable detection, to effectively exploit the shape feature and texture feature simultaneously.
Experimental results on 14 379 well labeled animals images validate the superiority of the proposed approach. Additionally,
we apply the animal head detector to improve the image search result through text based online photo search result
filtering.

62 Fuzzy Random Impulse Noise Removal From Color Image Sequences

In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with
different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One
strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail.
Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very
useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are
filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive
mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both
visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio
(PSNR) and the normalized color difference (NCD).

63 Geodesic Active Fields—A Geometric Framework for Image Registration

In this paper we present a novel geometric framework called geodesic active fields for general image registration. In image
registration, one looks for the underlying deformation field that best maps one image onto another. This is a classic ill-
posed inverse problem, which is usually solved by adding a regularization term. Here, we propose a multiplicative coupling
between the registration term and the regularization term, which turns out to be equivalent to embed the deformation field
in a weighted minimal surface problem. Then, the deformation field is driven by a minimization flow toward a harmonic map
corresponding to the solution of the registration problem. This proposed approach for registration shares close similarities
with the well-known geodesic active contours model in image segmentation, where the segmentation term (the edge
detector function) is coupled with the regularization term (the length functional) via multiplication as well. As a matter of
fact, our proposed geometric model is actually the exact mathematical generalization to vector fields of the weighted length
problem for curves and surfaces introduced by Caselles-Kimmel-Sapiro [1]. The energy of the deformation field is
measured with the Polyakov energy weighted by a suitable image distance, borrowed from standard registration models.
We investigate three different weighting functions, the squared error and the approximated absolute error for monomodal
images, and the local joint entropy for multimodal images. As compared to specialized state-of-the-art methods tailored for
specific applications, our geometric framework involves important contributions. Firstly, our general formulation for
registration works on any parametrizable, smooth and differentiable surface, including nonflat and multiscale images. In the
latter case, multiscale images are registered at all scales simultaneously, and the relations between space and scale are
intrinsically being accounted for. Second, this method is, to the best of our knowledge, the first reparametrization invariant
registration method introduced in the literature. Thirdly, the multiplicative coupling between the registration term, i.e. local
image discrepancy, and the regularization term naturally results in a data-dependent tuning of the regularization strength.
Finally, by choosing the metric on the deformation field one can freely interpolate between classic Gaussian and more


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interesting anisotropic, TV-like regularization.

64 Geometric Calibration of Lens and Filter Distortions for Multispectral Filter-Wheel Cameras

High-fidelity color image acquisition with a multispectral camera utilizes optical filters to separate the visible
electromagnetic spectrum into several passbands. This is often realized with a computer-controlled filter wheel, where each
position is equipped with an optical bandpass filter. For each filter wheel position, a grayscale image is acquired and the
passbands are finally combined to a multispectral image. However, the different optical properties and non-coplanar
alignment of the filters cause image aberrations since the optical path is slightly different for each filter wheel position. As
in a normal camera system, the lens causes additional wavelength-dependent image distortions called chromatic
aberrations. When transforming the multispectral image with these aberrations into an RGB image, color fringes appear,
and the image exhibits a pincushion or barrel distortion. In this paper, we address both the distortions caused by the lens
and by the filters. Based on a physical model of the bandpass filters, we show that the aberrations caused by the filters can
be modeled by displaced image planes. The lens distortions are modeled by an extended pinhole camera model, which
results in a remaining mean calibration error of only 0.07 pixels. Using an absolute calibration target, we then geometrically
calibrate each passband and compensate for both lens and filter distortions simultaneously. We show that both types of
aberrations can be compensated and present detailed results on the remaining calibration errors.

65 Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field
which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized
interactions between the relative geometries of the deformable model and the object boundary characterized by image
gradient. The evolution of the deformable model is solved using the level set method so that topological changes are
handled automatically. The relative geometrical configurations between the deformable model and the object boundaries
contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically
induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring
complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization
configurations. The voxel interactions across the whole image domain provide a global view of the object boundary
representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the
new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and
broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-
preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on
the segmentation of various geometries with different topologies from both synthetic and real images, and show that the
proposed method achieves significant improvements against existing image gradient techniques.


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66 Goal-Oriented Rectification of Camera-Based Document Images

Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer
from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we
present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to
improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished
with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D
rectangular area. The projection of the curved surface on the plane is guided only by the textual content’s appearance in
the document image while incorporating a transformation which does not depend on specific model primitives or camera
setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the
document image. Experimental results on various document images with a variety of distortions demonstrate the
robustness and effectiveness of the proposed rectification methodology using a consistent evaluation methodology that
encounters OCR accuracy and a newly introduced measure using a semi-automatic procedure.

67 Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement

In this paper, we propose a novel generic image prior—gradient profile prior, which implies the prior knowledge of natural
image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient
magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model.
Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which
are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient
fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness
enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The
reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.

68 Graph Cuts for Curvature Based Image Denoising

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV
minimization problems and binary MRF models has been much explored. This has resulted in some very efficient
combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome
limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order
derivatives have been proposed. The Euler’s elastica model is one such higher order model of central importance, which
minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such
higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization
algorithm based upon graph cuts for minimizing the energy in the Euler’s elastica model, by simplifying the problem to that
of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flowof the
energy function, and converges to a minimum point. The numerical experiments show that our new approach is more
effective in maintaining smooth visual results while preserving sharp features better than TV models.


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69 Graph Regularized Sparse Coding for Image Representation

Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm,
which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set.
Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications.
However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In
many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-
dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination.
In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse
representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a
smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The
extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed
algorithm.

70 HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation

Automatic image registration is still an actual challenge in several fields. Although several methods for automatic image
registration have been proposed in the last few years, it is still far from a broad use in several applications, such as in
remote sensing. In this paper, a method for automatic image registration through histogram-based image segmentation
(HAIRIS) is proposed. This new approach mainly consists in combining several segmentations of the pair of images to be
registered, according to a relaxation parameter on the histogram modes delineation (which itself is a new approach),
followed by a consistent characterization of the extracted objects—through the objects area, ratio between the axis of the
adjust ellipse, perimeter and fractal dimension—and a robust statistical based procedure for objects matching. The
application of the proposed methodology is illustrated to simulated rotation and translation. The first dataset consists in a
photograph and a rotated and shifted version of the same photograph, with different levels of added noise. It was also
applied to a pair of satellite images with different spectral content and simulated translation, and to real remote sensing
examples comprising different viewing angles, different acquisition dates and different sensors. An accuracy below 1 for
rotation and at the subpixel level for translation were obtained, for the most part of the considered situations. HAIRIS allows
for the registration of pairs of images (multitemporal and multisensor) with differences in rotation and translation, with
small differences in the spectral content, leading to a subpixel accuracy.

71 High Capacity Color Barcodes: Per Channel Data Encoding via Orientation Modulation in Elliptical Dot Arrays

We present a new high capacity color barcode. The barcode we propose uses the cyan, magenta, and yellow (C,M,Y)
colorant separations available in color printers and enables high capacity by independently encoding data in each of these
separations. In each colorant channel, payload data is conveyed by using a periodic array of elliptically shaped dots whose
individual orientations are modulated to encode the data. The orientation based data encoding provides beneficial
robustness against printer and scanner tone variations. The overall color barcode is obtained when these color separations
are printed in overlay as is common in color printing. A reader recovers the barcode data from a conventional color scan of
the barcode, using red, green, and blue (R,G,B) channels complementary, respectively, to the print C, M, and Y channels.
For each channel, first the periodic arrangement of dots is exploited at the reader to enable synchronization by


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compensating for both global rotation/scaling in scanning and local distortion in printing. To overcome the color
interference resulting from colorant absorptions in noncomplementary scanner channels, we propose a novel interference
minimizing data encoding approach and a statistical channel model (at the reader) that captures the characteristics of the
interference, enabling more accurate data recovery. We also employ an error correction methodology that effectively
utilizes the channel model. The experimental results show that the proposed method works well, offering (error-free)
operational rates that are comparable to or better than the highest capacity barcodes known in the literature.

72 High Dynamic Range Image Display With Halo and Clipping Prevention

The dynamic range of an image is defined as the ratio between the highest and the lowest luminance level. In a high
dynamic range (HDR) image, this value exceeds the capabilities of conventional display devices; as a consequence,
dedicated visualization techniques are required. In particular, it is possible to process an HDR image in order to reduce its
dynamic range without producing a significant change in the visual sensation experienced by the observer. In this paper,
we propose a dynamic range reduction algorithm that produces high-quality results with a low computational cost and a
limited number of parameters. The algorithm belongs to the category of methods based upon the Retinex theory of vision
and was specifically designed in order to prevent the formation of common artifacts, such as halos around the sharp edges
and clipping of the highlights, that often affect methods of this kind. After a detailed analysis of the state of the art, we shall
describe the method and compare the results and performance with those of two techniques recently proposed in the
literature and one commercial software.

73 High-Resolution Imaging Via Moving Random Exposure and Its Simulation

In this correspondence, we introduce a new imaging method to obtain high-resolution (HR) images. The image acquisition
is performed in two stages, compressive measurement and optimization reconstruction. In order to reconstruct HR images
by a small number of sensors, compressive measurements aremade. Specifically, compressive measurements are made by
a low-resolution (LR) camera with randomly fluttering shutter, which can be viewed as a moving random exposure pattern.
In the optimization reconstruction stage, the HR image is computed by different models according to the prior knowledge of
scenes. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes when the
camera resolution is limited by severe constraints such as cost, battery capacity, memory space, transmission bandwidth,
etc. and when the prior knowledge of scenes is available. The simulation results demonstrate the effectiveness of the
proposed imaging method.

74 Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts

Human pose estimation via motion tracking systems can be considered as a regression problem within a discriminative
framework. It is always a challenging task to model the mapping from observation space to state space because of the
high-dimensional characteristic in the multimodal conditional distribution. In order to build the mapping, existing
techniques usually involve a large set of training samples in the learning process which are limited in their capability to
deal with multimodality. We propose, in this work, a novel online sparse Gaussian Process (GP) regression model to
recover 3-D human motion in monocular videos. Particularly, we investigate the fact that for a given test input, its output is
mainly determined by the training samples potentially residing in its local neighborhood and defined in the unified input-
output space. This leads to a local mixture GP experts system composed of different local GP experts, each of which


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dominates a mapping behavior with the specific covariance function adapting to a local region. To handle the
multimodality, we combine both temporal and spatial information therefore to obtain two categories of local experts. The
temporal and spatial experts are integrated into a seamless hybrid system, which is automatically self-initialized and robust
for visual tracking of nonlinear human motion. Learning and inference are extremely efficient as all the local experts are
defined online within very small neighborhoods. Extensive experiments on two real-world databases, HumanEva and PEAR,
demonstrate the effectiveness of our proposed model, which significantly improve the performance of existing models.

75 Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints

Generalized morphological component analysis (GMCA) is a recent algorithm for multichannel data analysis which was
used successfully in a variety of applications including multichannel sparse decomposition, blind source separation (BSS),
color image restoration and inpainting. Building on GMCA, the purpose of this contribution is to describe a new algorithm
for BSS applications in hyperspectral data processing. It assumes the collected data is a mixture of components exhibiting
sparse spectral signatures as well as sparse spatial morphologies, each in specified dictionaries of spectral and spatial
waveforms. We report on numerical experiments with synthetic data and application to real observations which
demonstrate the validity of the proposed method.

76 Image Denoising in Mixed Poisson–Gaussian Noise

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding
algorithms for denoising images corrupted by mixed Poisson–Gaussian noise. We express the denoising process as a
linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-
squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson–Gaussian unbiased risk estimate). We provide
a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain
thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-
adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We
finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques
that are specifically tailored to the estimation of Poisson intensities.We also present denoising results obtained on real
images of low-count fluorescence microscopy.

77 IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the
high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are
enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to
decompose an input image into different subbands. Then the high frequency subbands as well as the input image are
interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained
through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT
(IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional
and state-of-art image resolution enhancement techniques.


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.
78 Image Segmentation Using Fuzzy Region Competition and Spatial/Frequency Information

This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image
segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term that
measures the conformity of spatial and frequency data within each region to (generalized) Gaussian densities whose parameters are
determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft
segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information
that can improve the overall segmentation result. To efficiently solve the minimization of the energy functional, we adopt an alternate
minimization procedure and make use of Chambolle’s fast duality projection algorithm. We apply the proposed method to synthetic and
natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising
segmentation performance compared with the current state-of-the-art approaches.

79 In Search of Perceptually Salient Groupings

Finding meaningful groupings of image primitives has been a long-standing problem in computer vision. This paper studies
how salient groupings can be produced using established theories in the field of visual perception alone. The major
contribution is a novel definition of the Gestalt principle of Prägnanz, based upon Koffka’s definition that image
descriptions should be both stable and simple. Our method is global in the sense that it operates over all primitives in an
image at once. It works regardless of the type of image primitives and is generally independent of image properties such as
intensity, color, and texture. A novel experiment is designed to quantitatively evaluate the groupings outputs by our
method, which takes human disagreement into account and is generic to outputs of any grouper. We also demonstrate the
value of our method in an image segmentation application and quantitatively show that segmentations deliver promising
results when benchmarked using the Berkeley Segmentation Dataset (BSDS).

80 Incremental Training of a Detector Using Online Sparse Eigen decomposition

The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently,
offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a
complete set of training data has to be collected beforehand. In addition, once learned, an offline detector cannot make use
of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: 1) the
technique should be computationally and storage efficient; 2) the updated classifier must maintain its high classification
accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse
linear discriminant analysis model. Unlike many existing online boosting detectors, which usually apply exponential or
logistic loss, our online algorithm makes use of linear discriminant analysis’ learning criterion that not only aims to
maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We
provide a better alternative for online boosting algorithms in the context of training a visual object detector.We demonstrate
the robustness and efficiency of our methods on handwritten digit and face data sets. Our results confirm that object
detection tasks benefit significantly when trained in an online manner.


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81 Information Content Weighting for Perceptual Image Quality Assessment

Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local
quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image
quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational
models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for
pooling should be proportional to local information content, which can be estimated in units of bit using advanced
statistical models of natural images. Our extensive studies based upon six publicly-available subject- rated image
databases concluded with three useful findings. First, information content weighting leads to consistent improvement in
the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized
peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-
art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale
structural similarity measures.

82 Interactive Streaming of Stored Multi view Video Using Redundant Frame Structures

While much of multiview video coding focuses on the rate-distortion performance of compressing all frames of all views for
storage or non-interactive video delivery over networks, we address the problem of designing a frame structure to enable
interactive multiview streaming, where clients can interactively switch views during video playback. Thus, as a client is
playing back successive frames (in time) for a given view, it can send a request to the server to switch to a different view
while continuing uninterrupted temporal playback. Noting that standard tools for random access (i.e., I-frame insertion) can
be bandwidth-inefficient for this application, we propose a redundant representation of I-, P-, and “merge” frames, where
each original picture can be encoded into multiple versions, appropriately trading off expected transmission rate with
storage, to facilitate view switching. We first present ad hoc frame structures with good performance when the view-
switching probabilities are either very large or very small.We then present optimization algorithms that generate more
general frame structures with better overall performance for the general case.We show in our experiments that we can
generate redundant frame structures offering a range of tradeoff points between transmission and storage, e.g.,
outperforming simple I-frame insertion structures by up to 45% in terms of bandwidth efficiency at twice the storage cost.

83 Inverse Half toning Based on the Bayesian Theorem

This study proposes a method which can generate high quality inverse halftone images from halftone images. This method
can be employed prior to any signal processing over a halftone image or the inverse halftoning used in JBIG2. The
proposed method utilizes the least-mean-square (LMS) algorithm to establish a relationship between the current processing
position and its corresponding neighboring positions in each type of halftone image, including direct binary search, error
diffusion, dot diffusion, and ordered dithering. After which, a referenced region called a support region (SR) is used to
extract features. The SR can be obtained by relabeling the LMS-trained filters with the order of importance. Moreover, the
probability of black pixel occurrence is considered as a feature in this work. According to this feature, the probabilities of


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all possible grayscale values at the current processing position can be obtained by the Bayesian theorem. Consequently,
the final output at this position is the grayscale value with the highest probability. Experimental results show that the
proposed method offers better visual quality than that of Mese–Vaidyanathan’s and Chang et al.’s methods in terms of
human-visual peak signal-to-noise ratio (HPSNR). In addition, the memory consumption is also superior to Mese–
Vaidyanathan’s method.

84 Iterative Shrinkage Approach to Restoration of Optical Imagery

The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central
importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the
resolution limitations of an imaging device in use and/or by the destructive influence of measurement noise. Specifically,
when the noise obeys a Poisson probability law, standard approaches to the problem of image reconstruction are based
upon using fixed-point algorithms which follow the methodology first proposed by Richardson and Lucy. The practice of
using these methods, however, shows that their convergence properties tend to deteriorate at relatively high noise levels.
Accordingly, in the present paper, a novel method for denoising and/or deblurring of digital images corrupted by Poisson
noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely
represented in the domain of a linear transform. Consequently, a shrinkagebased iterative procedure is proposed, which
guarantees the solution to converge to the global maximizer of an associated maximum a posteriori criterion. It is shown in
a series of computer-simulated experiments that the proposed method outperforms a number of existing alternatives in
terms of stability, precision, and computational efficiency.

85 JPEG2000-Based Scalable Interactive Video (JSIV)

We propose a novel paradigm for interactive video streaming and we coin the term JPEG2000-based scalable interactive
video (JSIV) for it. JSIV utilizes JPEG2000 to independently compress the original video sequence frames and provide for
quality and spatial resolution scalability. To exploit interframe redundancy, JSIV utilizes prediction and conditional
replenishment of code-blocks aided by a server policy that optimally selects the number of quality layer for each code-
block transmitted and a client policy that makes most of the received (distorted) frames. It is also possible for JSIV to
employ motion compensation; however, we leave this topic to future work. To optimally solve the server transmission
problem, a Lagrangian-style rate-distortion optimization procedure is employed. In JSIV, a wide variety of frame prediction
arrangements can be employed including hierarchical B-frames of the scalable video coding (SVC) extension of the
H.264/AVC standard. JSIV provides considerably better interactivity compared to existing schemes and can adapt
immediately to interactive changes in client interests, such as forward or backward playback and zooming into individual
frames. Experimental results for surveillance footage, which does not suffer from the absence of motion compensation,
show that JSIV’s performance is comparable to that of SVC in some usage scenarios while JSIV performs better in others.

86 Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations

This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF)
analysis. The kernel versions are based upon a dual formulation also termed Q-mode analysis in which the data enter into
the analysis via inner products in the Gram matrix only. In the kernel version, the inner products of the original data are
replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also


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known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all
quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the
nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF, and kernel MNF analyses handle
nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and
then performing a linear analysis in that space. Three examples show the very successful application of kernel MAF/MNF
analysis to: 1) change detection in DLR 3K camera data recorded 0.7 s apart over a busy motorway, 2) change detection in
hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown,
the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading
kernel MAF/MNF variates seem to possess the ability to adapt to even abruptly varying multi and hypervariate backgrounds
and focus on extreme observations.

87 Large Disparity Motion Layer Extraction via Topological Clustering

In this paper, we present a robust and efficient approach to extract motion layers from a pair of images with large disparity
motion. First, motion models are established as: 1) initial SIFT matches are obtained and grouped into a set of clusters
using our developed topological clustering algorithm; 2) for each cluster with no less than three matches, an affine
transformation is estimated with least-square solution as tentative motion model; and 3) the tentative motion models are
refined and the invalid models are pruned. Then, with the obtained motion models, a graph cuts based layer assignment
algorithm is employed to segment the scene into several motion layers. Experimental results demonstrate that our method
can successfully segment scenes containing objects with large interframe motion or even with significant interframe scale
and pose changes. Furthermore, compared with the previous method invented by Wills et al. and its modified version, our
method is much faster and more robust.

88 Correspondence Lazy Sliding Window Implementation of the Bilateral Filter on Parallel Architectures

Bilateral filter is one of the state-of-the-art methods for noise reduction in images. The plausible visual result the filter
produces makes it a common choice for image and video processing applications, yet, its high computational complexity
makes a real-time implementation a challenging task. Presented here is a parallel version of the bilateral filter using a lazy
sliding window, suitable for SIMD-type architectures.

89 Light Field Analysis for Modeling Image Formation

Image formation is traditionally described by a number of individual models, one for each specific effect in the image
formation process. However, it is difficult to aggregate the effects by concatenating such individual models. In this paper,
we apply light transport analysis to derive a unified image formation model that represents the radiance along a light ray as
a 4-D light field signal and physical phenomena such as lens refraction and blocking as linear transformations or
modulations of the light field. This unified mathematical framework allows the entire image formation process to be


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elegantly described by a single equation. It also allows most geometric and photometric effects of imaging, including
perspective transformation, defocus blur, and vignetting, to be represented in both 4-D primal and dual domains. The result
matches that of traditional models. Generalizations and applications of this theoretic framework are discussed.

90 Lightweight Detection of Additive Watermarking in the DWT-Domain

This article aims at lightweight, blind detection of additive spread-spectrum watermarks in the DWT domain.We focus on
two host signal noise models and two types of hypothesis tests for watermark detection. As a crucial point of our work we
take a closer look at the computational requirements of watermark detectors. This involves the computation of the
detection response, parameter estimation and threshold selection. We show that by switching to approximate host signal
parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance
without sacrificing detection performance. Our experimental results on a large number of images confirm the assumption
that there is not necessarily a tradeoff between computation time and detection performance.

91 Measuring the Quality of Quality Measures

Print quality (PQ) is a composite attribute defined by human perception. As such, the ultimate way to determine and
quantify PQ is by human survey. However, repeated surveys are time consuming and often represent a burden on
processes that involve repeated evaluations. A desired alternative would be an automatic quality rating tool. Once such
quality evaluation measure is proposed, it should be qualified. That is, it should be shown to reflect human assessment. If
two of the human opinions conflict, the tool cannot possibly agree with both. Conflicts between human opinions are
common, which complicates the evaluation of tool’s success in reflecting human judgment. There are many optional ways
for measuring the agreement between human assessment and tool evaluation, but different methods may have conflicting
results. It is, therefore, important to pre-establish the appropriate method for the evaluation of quality-evaluation-tools, a
method that takes the disagreement among the survey participants into account. In this paper, we model human quality
preference and derive the most appropriate method to qualify quality evaluation tools.We demonstrate the resulting
qualification method in a real life scenario—the qualification of the mechanical band meter.

92 Missing Intensity Interpolation Using a Kernel PCA-Based POCS Algorithm and its Applications

A missing intensity interpolation method using a kernel principal component analysis (PCA)-based projection onto convex
sets (POCS) algorithm and its applications are presented in this paper. In order to interpolate missing intensities within a
target image, the proposed method reconstructs local textures containing the missing pixels by using the POCS algorithm.
In this reconstruction process, a nonlinear eigenspace is constructed from each kind of texture, and the optimal subspace
for the target local texture is introduced into the constraint of the POCS algorithm. In the proposed method, the optimal
subspace can be selected by monitoring errors converged in the reconstruction process. This approach provides a solution
to the problem in conventional methods of not being able to effectively perform adaptive reconstruction of the target
textures due to missing intensities, and successful interpolation of the missing intensities by the proposed method can be
realized. Furthermore, since our method can restore any images including arbitrary-shaped missing areas, its potential in
two image reconstruction tasks, image enlargement and missing area restoration, is also shown in this paper.


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93 Multidimensional Filter Bank Signal Reconstruction From Multichannel Acquisition

We study the theory and algorithms of an optimal use of multidimensional signal reconstruction from multichannel
acquisition by using a filter bank setup. Suppose that we have an N-channel convolution system, referred to as analysis
filters, in M dimensions. Instead of taking all the data and applying multichannel deconvolution, we first reduce the
collected data set by an integer M * M uniform sampling matrix D, and then search for a synthesis polyphase matrix which
could perfectly reconstruct any input discrete signal. First, we determine the existence of perfect reconstruction (PR)
systems for a given set of finite-impulse response (FIR) analysis filters. Second, we present an efficient algorithm to find a
sampling matrix with maximum sampling rate and to find a FIR PR synthesis polyphase matrix for a given set of FIR
analysis filters. Finally, once a particular FIR PR synthesis polyphase matrix is found, we can characterize all FIR PR
synthesis matrices, and then find an optimal one according to design criteria including robust reconstruction in the
presence of noise.

94 Multiple Player Tracking in Sports Video: A Dual-Mode Two-Way Bayesian Inference Approach With Progressive Observation
Modeling

Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In
this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the
abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new
MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the
observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking
observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian
inference approach which dynamically switches between an offline general model and an online dedicated model to deal
with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward
smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey,
demonstrate the effectiveness and efficiency of the proposed method.

95 Multiregion Image Segmentation by Parametric Kernel Graph Cuts

The purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data.
The image data is transformed implicitly by a kernel function so that the piecewise constant model of the graph cut
formulation becomes applicable. The objective function contains an original data term to evaluate the deviation of the


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transformed data, within each segmentation region, from the piecewise constant model, and a smoothness, boundary
preserving regularization term. The method affords an effective alternative to complex modeling of the original image data
while taking advantage of the computational benefits of graph cuts. Using a common kernel function, energy minimization
typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point
computation. A quantitative and comparative performance assessment is carried out over a large number of experiments
using synthetic grey level data as well as natural images from the Berkeley database. The effectiveness of the method is
also demonstrated through a set of experiments with real images of a variety of types such as medical, synthetic aperture
radar, and motion maps.

96 Nonlocal Mumford-Shah Regularizers for Color Image Restoration

We propose here a class of restoration algorithms for color images, based upon the Mumford-Shah (MS) model and
nonlocal image information. The Ambrosio-Tortorelli and Shah elliptic approximations are defined to work in a small local
neighborhood, which are sufficient to denoise smooth regions with sharp boundaries. However, texture is nonlocal in
nature and requires semilocal/non-local information for efficient image denoising and restoration. Inspired from recent
works (nonlocal means of Buades, Coll, Morel, and nonlocal total variation of Gilboa, Osher), we extend the local Ambrosio-
Tortorelli and Shah approximations to MS functional (MS) to novel nonlocal formulations, for better restoration of fine
structures and texture. We present several applications of the proposed nonlocal MS regularizers in image processing such
as color image denoising, color image deblurring in the presence of Gaussian or impulse noise, color image inpainting,
color image super-resolution, and color filter array demosaicing. In all the applications, the proposed nonlocal regularizers
produce superior results over the local ones, especially in image inpainting with large missing regions. We also prove
several characterizations of minimizers based upon dual norm formulations.

97 Nonlocal PDEs-Based Morphology on Weighted Graphs for Image and Data Processing

Mathematical morphology (MM) offers a wide range of operators to address various image processing problems. These
operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In this paper, we
introduce a nonlocal PDEs-based morphological framework defined on weighted graphs. We present and analyze a set of
operators that leads to a family of discretized morphological PDEs on weighted graphs. Our formulation introduces
nonlocal patch-based configurations for image processing and extends PDEs-based approach to the processing of
arbitrary data such as nonuniform high dimensional data. Finally, we show the potentialities of our methodology in order to
process, segment and classify images and arbitrary data.

98 Non rigid Registration of 2-D and 3-D Dynamic Cell Nuclei Images for Improved Classification of Sub-cellular Particle Motion

The observed motion of subcellular particles in fluorescence microscopy image sequences of live cells is generally a
superposition of the motion and deformation of the cell and the motion of the particles. Decoupling the two types of
movements to enable accurate classification of the particle motion requires the application of registration algorithms. We
have developed an intensity-based approach for nonrigid registration of multichannel microscopy image sequences of cell
nuclei. First, based on 3-D synthetic images we demonstrate that cell nucleus deformations change the observed motion
types of particles and that our approach allows to recover the original motion. Second, we have successfully applied our
approach to register 2-D and 3-D real microscopy image sequences. A quantitative experimental comparison with previous


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approaches for nonrigid registration of cell microscopy has also been performed.

99 Non-uniform Directional Filter Banks With Arbitrary Frequency Partitioning

Directional filter banks (DFBs) are highly desired in directional representation of images. In this correspondence, we
propose a 2-D nonsubsampled nonuniform directional filter bank (NUDFB) and its design method. The proposed NUDFB
has nonuniform wedge-shaped subbands and allows arbitrary frequency partitioning schemes. It can extract directional
information according to the directional distribution of images. This attractive advantage cannot be achieved by the
existing directional transforms. The design method of the proposed NUDFB is based upon the pseudopolar Fourier
transform. By utilizing the geometry property of the pseudopolar grid, we employ a 1-D nonsubsampled nonuniform filter
bank to obtain a set of nonuniform wedge-shaped subbands. During the design process, only 1-D operations are involved
and, thus, the difficulty encountered in the design of 2-D fan filters is avoided. To demonstrate the potential of the proposed
NUDFB, an example on image directional decomposition is given.

100 No-Reference Blur Assessment of Digital Pictures Based on Multi-feature Classifiers

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start
with the generation of a large real image database containing pictures taken by human users in a variety of situations, and
the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth,
we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur
(gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-
art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation
in which an effective method is pursued by combining several metrics and low-level image features.We test this paradigm
by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a
classifier based upon a neural network structure. Experimental results show that this leads to an improved performance
that better reflects the images’ ground truth. Finally, based upon the real image database, we show that the proposed
method also outperforms other algorithms and metrics in realistic blur scenarios.

101 On a Derivative-Free Fan-Beam Reconstruction Formula

We clarify that the derivative-free fan-beam reconstruction formula [IEEE Trans. Image Process. 2, 543–547, 1993] only
allows exact reconstruction of an object for a circular trajectory or at the origin of the coordinate system for a radially
symmetric noncircular trajectory.

102 On the Selection of Optimal Feature Region Set for Robust Digital Image Watermarking

A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims
to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve
image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some


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predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then
adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined
attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the
primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a
multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for
StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the
predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-
known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

103 Online Sparse Gaussian Process Regression and Its Applications

We present a new Gaussian process (GP) inference algorithm, called online sparse matrix Gaussian processes (OSMGP),
and demonstrate its merits by applying it to the problems of head pose estimation and visual tracking. The OSMGP is based
upon the observation that for kernels with local support, the Gram matrix is typically sparse. Maintaining and updating the
sparse Cholesky factor of the Gram matrix can be done efficiently using Givens rotations. This leads to an exact, online
algorithm whose update time scales linearly with the size of the Gram matrix. Further, we provide a method for constant
time operation of the OSMGP using matrix downdates. The downdates maintain the Cholesky factor at a constant size by
removing certain rows and columns corresponding to discarded training examples. We demonstrate that, using these
matrix downdates, online hyperparameter estimation can be included at cost linear in the number of total training examples.
We describe a robust appearance-based head pose estimation system based upon the OSMGP. Numerous experiments and
comparisons with existing methods using a large dataset system demonstrate the efficiency and accuracy of our system.
Further, to showcase the applicability of OSMGP to a wide variety of problems, we also describe a regression-based visual
tracking method. Experiments show that our OSMGP algorithm generalizes well using online learning.

104 Optimal Design of FIR Triplet Halfband Filter Bank and Application in Image Coding

This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear
phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed
regularity order. The design problem is formulated as the minimization of the least square error subject to peak error
constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-
infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be
solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison
with previous works. Finally, the image coding performance of the filter bank is presented.

105 Optimal Image Alignment With Random Projections of Manifolds: Algorithm and Geometric Analysis

This paper addresses the problem of image alignment based on random measurements. Image alignment consists of
estimating the relative transformation between a query image and a reference image.We consider the specific problem
where the query image is provided in compressed form in terms of linear measurements captured by a vision sensor.We


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cast the alignment problem as a manifold distance minimization problem in the linear subspace defined by the
measurements. The transformation manifold that represents synthesis of shift, rotation, and isotropic scaling of the
reference image can be given in closed form when the reference pattern is sparsely represented over a parametric
dictionary. We show that the objective function can then be decomposed as the difference of two convex functions (DC) in
the particular case where the dictionary is built on Gaussian functions. Thus, the optimization problem becomes a DC
program, which in turn can be solved globally by a cutting plane method. The quality of the solution is typically affected by
the number of random measurements and the condition number of the manifold that describes the transformations of the
reference image. We show that the curvature, which is closely related to the condition number, remains bounded in our
image alignment problem, which means that the relative transformation between two images can be determined optimally in
a reduced subspace.

106 Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising

The removal of Poisson noise is often performed through the following three-step procedure. First, the noise variance is
stabilized by applying the Anscombe root transformation to the data, producing a signal in which the noise can be treated
as additive Gaussian with unitary variance. Second, the noise is removed using a conventional denoising algorithm for
additive white Gaussian noise. Third, an inverse transformation is applied to the denoised signal, obtaining the estimate of
the signal of interest. The choice of the proper inverse transformation is crucial in order to minimize the bias error which
arises when the nonlinear forward transformation is applied. We introduce optimal inverses for the Anscombe
transformation, in particular the exact unbiased inverse, a maximum likelihood (ML) inverse, and a more sophisticated
minimum mean square error (MMSE) inverse. We then present an experimental analysis using a few state-of-the-art
denoising algorithms and show that the estimation can be consistently improved by applying the exact unbiased inverse,
particularly at the low-count regime. This results in a very efficient filtering solution that is competitive with some of the
best existing methods for Poisson image denoising.

107 Optimizing a Tone Curve for Backward-Compatible High Dynamic Range Image and Video Compression

For backward compatible high dynamic range (HDR) video compression, the HDR sequence is reconstructed by inverse
tone-mapping a compressed low dynamic range (LDR) version of the original HDR content. In this paper, we show that the
appropriate choice of a tone-mapping operator (TMO) can significantly improve the reconstructed HDR quality. We develop
a statistical model that approximates the distortion resulting from the combined processes of tone-mapping and
compression. Using this model, we formulate a numerical optimization problem to find the tone-curve that minimizes the
expected mean square error (MSE) in the reconstructed HDR sequence. We also develop a simplified model that reduces
the computational complexity of the optimization problem to a closed-form solution. Performance evaluations show that the
proposed methods provide superior performance in terms of HDR MSE and SSIM compared to existing tone-mapping
schemes. It is also shown that the LDR image quality resulting from the proposed methods matches that produced by
perceptually-based TMOs.

108 Paramer Mismatch-Based Spectral Gamut Mapping

Aspectral agreement between the original scene and a printed reproduction is required to achieve an illuminant-invariant
visual match. This is usually impossible since the spectral gamut of typical printing systems is only a small subset of all
natural reflectances. Out-of gamut reflectances need to be mapped into the spectral gamut of the printer minimizing the


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perceived error between original and reproduction for more than one illuminant. In this paper, we propose an algorithmic
framework for spectral gamut mapping to achieve a reproduction that is as visually correct as a colorimetric reproduction
for one illuminant and is superior for a set of other illuminants. A sequence of hierarchical mappings in 3-D color spaces
are performed utilizing the observer’s color quantization to increase the spectral variability of subsequent transformations:
For the most important illuminant a traditional colorimetric gamut mapping is performed. For any additional illuminants
colors are mapped onto pixel-dependent paramer mismatch gamuts preserving the visual equivalence of previous
transformations. We present a separation method for investigating the spectral gamut mapping framework and show that
hue shifts and chroma gains cannot be always avoided for the second and subsequent illuminants and that the order of
illuminants has a large impact on the final reproduction.

109 Passive Polarimetric Imagery-Based Material Classification Robust to Illumination Source Position and Viewpoint

Polarization, a property of light that conveys information about the transverse electric field orientation, complements other
attributes of electromagnetic radiation such as intensity and frequency. Using multiple passive polarimetric images, we
develop an iterative, model-based approach to estimate the complex index of refraction and apply it to target classification.

110 Perceptual Segmentation: Combining Image Segmentation With Object Tagging

Human observers understand the content of an image intuitively. Based upon image content, they perform many
imagerelated tasks, such as creating slide shows and photo albums, and organizing their image archives. For example, to
select photos for an album, people assess image quality based upon the main objects in the image. They modify colors in
an image based upon the color of important objects, such as sky, grass or skin. Serious photographers might modify each
object separately. Photo applications, in contrast, use low-level descriptors to guide similar tasks. Typical descriptors, such
as color histograms, noise level, JPEG artifacts and overall sharpness, can guide an imaging application and safeguard
against blunders. However, there is a gap between the outcome of such operations and the same task performed by a
person. We believe that the gap can be bridged by automatically understanding the content of the image. This paper
presents algorithms for automatic tagging of perceptual objects in images, including sky, skin, and foliage, which
constitutes an important step toward this goal.

111 Performance Analysis of n-Channel Symmetric FEC-Based Multiple Description Coding for OFDM Networks

Recently, multiple description source coding has emerged as an attractive framework for robust multimedia transmission
over packet erasure channels. In this paper, we mathematically analyze the performance of n-channel symmetric FEC-based
multiple description coding for a progressive mode of transmission over orthogonal frequency division multiplexing
(OFDM) networks in a frequency-selective slowly-varying Rayleigh faded environment. We derive the expressions for the
bounds of the throughput and distortion performance of the system in an explicit closed form, whereas the exact
performance is given by an expression in the form of a single integration. Based on this analysis, the performance of the
system can be numerically evaluated. Our results show that at high SNR, the multiple description encoder does not need to
fine-tune the optimization parameters of the system due to the correlated nature of the subcarriers. It is also shown that,
despite the bursty nature of the errors in a slow fading environment, FEC-based multiple description coding without


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temporal coding provides a greater advantage for smaller description sizes.

112 Practical Bounds on Image Denoising: From Estimation to Information

Recently, in a previous work, we proposed a way to bound how well any given image can be denoised. The bound was
computed directly from the noise-free image that was assumed to be available. In this work, we extend the formulation to
the more practical case where no ground truth is available.We show that the parameters of the bounds, namely the cluster
covariances and level of redundancy for patches in the image, can be estimated directly from the noise corrupted image.
Further, we analyze the bounds formulation to show that these two parameters are interdependent and they, along with the
bounds formulation as a whole, have a nice information-theoretic interpretation as well. The results are verified through a
variety of well-motivated experiments.

113 Proto-Object Based Rate Control for JPEG2000: An Approach to Content-Based Scalability

The JPEG2000 system provides scalability with respect to quality, resolution and color component in the transfer of
images. However, scalability with respect to semantic content is still lacking. We propose a biologically plausible salient
region based bit allocation mechanism within the JPEG2000 codec for the purpose of augmenting scalability with respect to
semantic content. First, an input image is segmented into several salient proto-objects (a region that possibly contains a
semantically meaningful physical object) and background regions (a region that contains no object of interest) by modeling
visual focus of attention on salient proto-objects. Then, a novel rate control scheme distributes a target bit rate to each
individual region according to its saliency, and constructs quality layers of proto-objects for the purpose of more precise
truncation comparable to original quality layers in the standard. Empirical results show that the suggested approach adds
to the JPEG2000 system scalability with respect to content as well as the functionality of selectively encoding, decoding,
and manipulation of each individual proto-object in the image, with only some slightly trivial modifications to the JPEG2000
standard. Furthermore, the proposed rate control approach efficiently reduces the computational complexity and memory
usage, as well as maintains the high quality of the image to a level comparable to the conventional post-compression rate
distortion (PCRD) optimum truncation algorithm for JPEG2000.

114 Quality Assessment of Deblocked Images

We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from
compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index,
named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including
a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement
with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked
images than PSNR and a well-known blockiness-specific index.

115 Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery


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N-finder algorithm (N-FINDR) has been widely used in endmember extraction. When it comes to implementation several
issues need to be addressed. One is determination of endmembers, required for N-FINDR to generate. Another is its
computational complexity resulting from an exhaustive search. A third one is its requirement of dimensionality reduction. A
fourth and probably the most critical issue is its use of random initial endmembers which results in inconsistent final
endmember selection and results are not reproducible. This paper re-invents the wheel by re-designing the N-FINDR in
such a way that all the above-mentioned issues can be resolved while making the last issue an advantage. The idea is to
implement the N-FINDR as a random algorithm, called random N-FINDR (RN-FINDR) so that a single run using one set of
random initial endmembers is considered as one realization. If there is an endmember present in the data, it should appear
in any realization regardless of what random set of initial endmembers is used. In this case, the N-FINDR is terminated
when the intersection of all realizations produced by two consecutive runs of RN-FINDR remains the same in which case
the p is then automatically determined by the intersection set without appealing for any criterion. In order to substantiate
the proposed RN-FINDR custom-designed synthetic image experiments with complete knowledge are conducted for
validation and real image experiments are also performed to demonstrate its utility in applications.

116 Random Phase Textures: Theory and Synthesis

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise
(RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They
arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical
analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes.
Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples
are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to
obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to
color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture
samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample.

117 Real-Time Discriminative Background Subtraction

The authors examine the problem of segmenting foreground objects in live video when background scene textures change
over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional—
yielding a local online discriminative algorithm that can quickly adapt to temporal changes. We analyze the algorithm’s
convergence, discuss its robustness to nonstationarity, and provide an efficient nonlinear extension via sparse kernels. To
accommodate interactions among neighboring pixels, a global algorithm is then derived that explicitly distinguishes
objects versus background using maximum a posteriori inference in a Markov random field (implemented via graph-cuts).
By exploiting the parallel nature of the proposed algorithms, we develop an implementation that can run efficiently on the
highly parallel graphics processing unit (GPU). Empirical studies on a wide variety of datasets demonstrate that the
proposed approach achieves quality that is comparable to state-of-the-art offline methods, while still being suitable for real-
time video analysis.


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118 Reference Sharing Mechanism for Watermark Self-Embedding

This paper proposes two novel self-embedding watermarking schemes based upon a reference sharing mechanism, in
which the watermark to be embedded is a reference derived from the original principal content in different regions and
shared by these regions for content restoration. After identifying tampered blocks, both the reference data and the original
content in the reserved area are used to recover the principal content in the tampered area. By using the first scheme, the
original data in five most significant bit layers of a cover image can be recovered and the original watermarked image can
also be retrieved when the content replacement is not too extensive. In the second scheme, the host content is
decomposed into three levels, and the reference sharing methods with different restoration capabilities are employed to
protect the data at different levels. Therefore, the lower the tampering rate, the more levels of content data are recovered,
and the better the quality of restored results.

119 Regularized Background Adaptation: A Novel Learning Rate Control Scheme for Gaussian Mixture Modeling

To model a scene for background subtraction, Gaussian mixture modeling (GMM) is a popular choice for its capability of
adaptation to background variations. However, GMM often suffers from a tradeoff between robustness to background
changes and sensitivity to foreground abnormalities and is inefficient in managing the tradeoff for various surveillance
scenarios. By reviewing the formulations of GMM, we identify that such a tradeoff can be easily controlled by adaptive
adjustments of the GMM’s learning rates for image pixels at different locations and of distinct properties. A new rate control
scheme based on high-level feedback is then developed to provide better regularization of background adaptation for GMM
and to help resolving the tradeoff. Additionally, to handle lighting variations that change too fast to be caught by GMM, a
heuristic rooting in frame difference is proposed to assist the proposed rate control scheme for reducing false foreground
alarms. Experiments show the proposed learning rate control scheme, together with the heuristic for adaptation of over-
quick lighting change, gives better performance than conventional GMM approaches.

120 Resolution Scalable Image Coding With Reversible Cellular Automata

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear
filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a
similar representation. The original image is decomposed into four subbands, such that one of them retains most of the
features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and
arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that
uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground
is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have
been designed. They provide compression performances very close to or even better than JBIG, depending upon the image
characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm
outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

121 Robust Principal Component Analysis Based on Maximum Correntropy Criterion


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Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we
present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization
algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced
to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the
following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a
heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data
mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix
corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to
deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust
rotational-invariant PCAs based on L1 norm when outliers occur.

122 Salient Motion Features for Video Quality Assessment

Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of
applications. Depending on the video content, the artifacts introduced by the coding process can be more or less
pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion
affects both human attention and coding quality, this relationship has only recently started gaining attention among the
research community, when video quality assessment (VQA) is concerned. In this paper, the effect of calculating several
objective measure features, related to video coding artifacts, separately for salient motion and other regions of the frames
of the sequence is examined. In addition, we propose a new scheme for quality assessment of coded video streams, which
takes into account salient motion. Standardized procedure has been used to calculate the Mean Opinion Score (MOS),
based on experiments conducted with a group of non-expert observers viewing standard definition (SD) sequences. MOS
measurements were taken for nine different SD sequences, coded using MPEG-2 at five different bit-rates. Eighteen
different published approaches related to measuring the amount of coding artifacts objectively on a single-frame basis
were implemented. Additional features describing the intensity of salient motion in the frames, as well as the intensity of
coding artifacts in the salient motion regions were proposed. Automatic feature selection was performed to determine the
subset of features most correlated to video quality. The results show that salient-motion-related features enhance
prediction and indicate that the presence of blocking effect artifacts and blurring in the salient regions and variance and
intensity of temporal changes in non-salient regions influence the perceived video quality.

123 Size-Controllable Region-of-Interest in Scalable Image Representation

Differentiating region-of-interest (ROI) from non-ROI in an image in terms of relative size as well as fidelity becomes an
important functionality for future visual communication environment with a variety of display devices. In this paper, we
propose a scalable image representation with the ROI functionality in the spatial domain, which allows us to generate a
hierarchy of images with arbitrary sizes. The ROI functionality of our scalable representation is a result of a nonuniform grid
transformation in the spatial domain, where only the center of ROI and an expansion parameter are to be known. Our grid
transformation guarantees no loss of information within the area of ROI.

124 Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference


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We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated
anchor signal that is contaminated with complex interference. The corruption in the anchor signal involves intensity
modulations, linear distortions, structured interference, clutter, and noise just to name a few. The proposed setup reflects
nontrivial prediction scenarios involving images and video frames where statistically related data is rendered ineffective for
traditional methods due to cross-fades, blends, clutter, brightness variations, focus changes, and other complex
transitions. Rather than trying to solve a difficult estimation problem involving nonstationary signal statistics, we obtain
simple predictors in linear transform domain where the underlying signals are assumed to be sparse. We show that these
simple predictors achieve surprisingly good performance and seamlessly allow successful predictions even under
complicated cases. None of the interference parameters are estimated as our algorithm provides completely blind and
automated operation. We provide a general formulation that allows for nonlinearities in the prediction loop and we consider
prediction optimal decompositions. Beyond an extensive set of results on prediction and registration, the proposed method
is also implemented to operate inside a state-of-the-art compression codec and results show significant improvements on
scenes that are difficult to encode using traditional prediction techniques.

125 Spatiotemporal Localization and Categorization of Human Actions in Unsegmented Image Sequences

In this paper we address the problem of localization and recognition of human activities in unsegmented image sequences.
The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the
activity which relies on the spatiotemporal localization of characteristic ensembles of feature descriptors. Evidence for the
spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. The local nature
of the proposed voting framework allows us to deal with multiple activities taking place in the same scene, as well as with
activities in the presence of clutter and occlusion. We use boosting in order to select characteristic ensembles per class.
This leads to a set of class specific codebooks where each codeword is an ensemble of features. During training, we store
the spatial positions of the codeword ensembles with respect to a set of reference points, as well as their temporal
positions with respect to the start and end of the action instance. During testing, each activated codeword ensemble casts
votes concerning the spatiotemporal position and extend of the action, using the information that was stored during
training. Mean Shift mode estimation in the voting space provides the most probable hypotheses concerning the
localization of the subjects at each frame, as well as the extend of the activities depicted in the image sequences. We
present classification and localization results for a number of publicly available datasets, and for a number of sequences
where there is a significant amount of clutter and occlusion.

126 Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation

In this paper, a structured max-margin learning algorithm is developed to achieve more effective training of a large number
of inter-related classifiers for multilabel image annotation application. To leverage multilabel images for classifier training,
each multilabel image is partitioned into a set of image instances (image regions or image patches) and an automatic
instance label identification algorithm is developed to assign multiple labels (which are given at the image level) to the most
relevant image instances. A K-way min-max cut algorithm is developed for automatic instance clustering and kernel weight
determination, where multiple base kernels are seamlessly combined to address the issue of huge intra-concept visual
diversity more effectively. Second, a visual concept network is constructed for characterizing the inter-concept visual
similarity contexts more precisely in the high-dimensional multimodal feature space. The visual concept network is used to
determine the inter-related learning tasks directly in the feature space rather than in the label space because feature space
is the common space for classifier training and image classification. Third, a parallel computing platform is developed to
achieve more effective learning of a large number of inter-related classifiers over the visual concept network. A structured


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max-margin learning algorithm is developed by incorporating the visual concept network, max-margin Markov networks
and multitask learning to address the issue of huge inter-concept visual similarity more effectively. By leveraging the inter-
concept visual similarity contexts for inter-related classifier training, our structured max-margin learning algorithm can
significantly enhance the discrimination power of the inter-related classifiers. Our experiments have also obtained very
positive results for a large number of object classes and image concepts.

127 Studentized Dynamical System for Robust Object Tracking

This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation.
Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual
tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model
the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA
is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although
Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely
limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very
sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails.
Recently, more heavy-tailed distributions (e.g., Student’s t-distribution) have been introduced to increase the robustness of
the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to
adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these
auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian.
Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise
and an improved tracking performance over DS with a Gaussian based observation model.

128 Sub-Hexagonal Phase Correlation for Motion Estimation

We present a novel frequency-domain motion estimation technique, which operates on hexagonal images and employs the
hexagonal Fourier transform. Our method involves image sampling on a hexagonal lattice followed by a normalised
hexagonal cross-correlation in the frequency domain. The term subpixel (or subcell) is defined on a hexagonal grid in order
to achieve floating point registration. Experiments using both artificially induced motion and actual motion demonstrate
that the proposed method outperforms the state-of-the-art in frequency-domain motion estimation operating on a square
lattice, in the shape of phase correlation, in terms of subpixel accuracy for a range of test material and motion scenarios.

129 Subpixel Registration With Gradient Correlation

We address the problem of subpixel registration of images assumed to be related by a pure translation. We present a
method which extends gradient correlation to achieve subpixel accuracy. Our scheme is based on modeling the dominant
singular vectors of the 2-D gradient correlation matrix with a generic kernel which we derive by studying the structure of
gradient correlation assuming natural image statistics. Our kernel has a parametric form which offers flexibility in modeling
the functions obtained from various types of image data.We estimate the kernel parameters, including the unknown
subpixel shifts, using the Levenberg-Marquardt algorithm. Experiments with LANDSAT and MRI data show that our scheme
outperforms recently proposed state-of-the-art phase correlation methods.


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130 Text From Corners: A Novel Approach to Detect Text and Caption in Videos

Detecting text and caption from videos is important and in great demand for video retrieval, annotation, indexing, and
content analysis. In this paper, we present a corner based approach to detect text and caption from videos. This approach
is inspired by the observation that there exist dense and orderly presences of corner points in characters, especially in text
and caption. We use several discriminative features to describe the text regions formed by the corner points. The usage of
these features is in a flexible manner, thus, can be adapted to different applications. Language independence is an
important advantage of the proposed method. Moreover, based upon the text features, we further develop a novel algorithm
to detect moving captions in videos. In the algorithm, the motion features, extracted by optical flow, are combined with text
features to detect the moving caption patterns. The decision tree is adopted to learn the classification criteria. Experiments
conducted on a large volume of real video shots demonstrate the efficiency and robustness of our proposed approaches
and the real-world system. Our text and caption detection system was recently highlighted in a worldwide multimedia
retrieval competition, Star Challenge, by achieving the superior performance with the top ranking.

131 The Roadmaker’s Algorithm for the Discrete Pulse Transform

The discrete pulse transform (DPT) is a decomposition of an observed signal into a sum of pulses, i.e., signals that are
constant on a connected set and zero elsewhere. Originally developed for 1-D signal processing, the DPT has recently been
generalized to more dimensions. Applications in image processing are currently being investigated. The time required to
compute the DPT as originally defined via the successive application of LULU operators (members of a class of minimax
filters studied by Rohwer) has been a severe drawback to its applicability. This paper introduces a fast method for obtaining
such a decomposition, called the Roadmaker’s algorithm because it involves filling pits and razing bumps. It acts
selectively only on those features actually present in the signal, flattening them in order of increasing size by subtracing an
appropriate positive or negative pulse, which is then appended to the decomposition. The implementation described here
covers 1-D signal as well as two and 3-D image processing in a single framework. This is achieved by considering the
signal or image as a function defined on a graph, with the geometry specified by the edges of the graph. Whenever a feature
is flattened, nodes in the graph are merged, until eventually only one node remains. At that stage, a new set of edges for the
same nodes as the graph, forming a tree structure, defines the obtained decomposition. The Roadmaker’s algorithm is
shown to be equivalent to the DPT in the sense of obtaining the same decomposition. However, its simpler operators are
not in general equivalent to the LULU operators in situations where those operators are not applied successively. A by-
product of the Roadmaker’s algorithm is that it yields a proof of the so-called Highlight Conjecture, stated as an open
problem in 2006. We pay particular attention to algorithmic details and complexity, including a demonstration that in the 1-D
case, and also in the case of a complete graph, the Roadmaker’s algorithm has optimal complexity: it runs in time ,
where is the number of arcs in the graph.

132 The Sparse Matrix Transform for Covariance Estimation and Analysis of High Dimensional Signals


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Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and
machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs
a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition
which can be represented as a sparse matrix transform (SMT). The SMT is formed by a product of pairwise coordinate
rotations known as Givens rotations. Using this framework, the covariance can be efficiently estimated using greedy
optimization of the log-likelihood function, and the number of Givens rotations can be efficiently computed using a cross-
validation procedure. The resulting estimator is generally positive definite and well-conditioned, even when the sample size
is limited. Experiments on a combination of simulated data, standard hyperspectral data, and face image sets show that the
SMT-based covariance estimates are consistently more accurate than both traditional shrinkage estimates and recently
proposed graphical lasso estimates for a variety of different classes and sample sizes. An important property of the new
covariance estimate is that it naturally yields a fast implementation of the estimated eigen-transformation using the SMT
representation. In fact, the SMT can be viewed as a generalization of the classical fast Fourier transform (FFT) in that it uses
“butterflies” to represent an orthonormal transform. However, unlike the FFT, the SMT can be used for fast eigen-signal
analysis of general non-stationary signals.

133 Tomographic Reconstruction of Gated Data Acquisition Using DFT Basis Functions

In image reconstruction gated acquisition is often used in order to deal with blur caused by organ motion in the resulting
images. However, this is achieved almost inevitably at the expense of reduced signal-to-noise ratio in the acquired data. In
this work, we propose a reconstruction procedure for gated images based upon use of discrete Fourier transform (DFT)
basis functions, wherein the temporal activity at each spatial location is regulated by a Fourier representation. The gated
images are then reconstructed through determination of the coefficients of the Fourier representation. We demonstrate this
approach in the context of single photon emission computed tomography (SPECT) for cardiac imaging, which is often
hampered by the increased noise due to gating and other degrading factors. We explore two different reconstruction
algorithms, one is a penalized least-square approach and the other is a maximum a posteriori approach. In our experiments,
we conducted a quantitative evaluation of the proposed approach using Monte Carlo simulated SPECT imaging. The results
demonstrate that use of DFT-basis functions in gated imaging can improve the accuracy of the reconstruction. As a
preliminary demonstration, we also tested this approach on a set of clinical acquisition.

134 Topological Well-Composedness and Glamorous Glue: A Digital Gluing Algorithm for Topologically Constrained Front Propagation

We propose a new approach to front propagation algorithms based on a topological variant of well-composedness which
contrasts with previous methods based on simple point detection. This provides for a theoretical justification, based on the
digital Jordan separation theorem, for digitally “gluing” evolved well-composed objects separated by well-composed
curves or surfaces. Additionally, our framework can be extended to more relaxed topologically constrained algorithms
based on multisimple points. For both methods this framework has the additional benefit of obviating the requirement for
both a user-specified connectivity and a topologically- consistent marching cubes/squares algorithm in meshing the
resulting segmentation.

135 Total Variation Projection With First Order Schemes

This article proposes a new algorithm to compute the projection on the set of images whose total variation is bounded by a


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constant. The projection is computed through a dual formulation that is solved by first order non-smooth optimization
methods. This yields an iterative algorithm that applies iterative soft thresholding to the dual vector field, and for which we
establish convergence rate on the primal iterates. This projection algorithm can then be used as a building block in a
variety of applications such as solving inverse problems under a total variation constraint, or for texture synthesis.
Numerical results are reported to illustrate the usefulness and potential applicability of our TV projection algorithm on
various examples including denoising, texture synthesis, inpainting, deconvolution and tomography problems. We also
show that our projection algorithm competes favorably with state-of-the-art TV projection methods in terms of convergence
speed.

136 Transferring Boosted Detectors Towards Viewpoint and Scene Adaptiveness

In object detection, disparities in distributions between the training samples and the test ones are often inevitable, resulting
in degraded performance for application scenarios. In this paper, we focus on the disparities caused by viewpoint and
scene changes and propose an efficient solution to these particular cases by adapting generic detectors, assuming
boosting style. A pretrained boosting-style detector encodes a priori knowledge in the form of selected features and weak
classifier weighting. Towards adaptiveness, the selected features are shifted to the most discriminative locations and
scales to compensate for the possible appearance variations. Moreover, the weighting coefficients are further adapted with
covariate boost, which maximally utilizes the related training data to enrich the limited new examples. Extensive
experiments validate the proposed adaptation mechanism towards viewpoint and scene adaptiveness and show
encouraging improvement on detection accuracy over state-of-the-art methods.

137 Unequal Protection of Video Data According to Slice Relevance

In this paper, we devise a procedure that mimics the behavior of a progressive video stream starting from a non
progressive one such as H.264/AVC encoded video. This allows one to unequally protect the video data in an efficient way,
according to their importance and the network state. The reported results demonstrate the superior performance of the
proposed approach in comparison to state-of-the-art methods for resilient transmission of H.264/AVC data. Moreover, the
flexibility in terms of redundancy insertion and achieved quality levels, allows one to span different applications, possibly
including P2P video streaming.

138 Uniform Motion Blur in Poissonian Noise: Blur/Noise Tradeoff

In this paper we consider the restoration of images corrupted by both uniform motion blur and Poissonian noise.We
formulate an image formation model that explicitly takes into account the length of the blur point-spread function and the
noise level as functions of the exposure time. Further, we present an analysis of the achievable restoration performance by
showing how the root mean squared error varies with respect to the exposure time. It turns out that the worst situations are
represented by either too short or too long exposure times. In between there exists an optimal exposure time that
maximizes the restoration performance, balancing the amount of blur and noise in the observation.We justify such result
through a mathematical analysis of the signal-to-noise ratio in Fourier domain; this study is then validated by deblurring
synthetic data as well as camera raw data.


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139 Variable Length Open Contour Tracking Using a Deformable Trellis

This paper focuses on contour tracking, an important problem in computer vision, and specifically on open contours that
often directly represent a curvilinear object. Compelling applications are found in the field of bioimage analysis where
blood vessels, dendrites, and various other biological structures are tracked over time. General open contour tracking, and
biological images in particular, pose major challenges including scene clutter with similar structures (e.g., in the cell), and
time varying contour length due to natural growth and shortening phenomena, which have not been adequately answered
by earlier approaches based on closed and fixed end-point contours.We propose a model-based estimation algorithm to
track open contours of time-varying length, which is robust to neighborhood clutter with similar structures. The method
employs a deformable trellis in conjunction with a probabilistic (hidden Markov) model to estimate contour position,
deformation, growth and shortening. It generates a maximum a posteriori estimate given observations in the current frame
and prior contour information from previous frames. Experimental results on synthetic and real-world data demonstrate the
effectiveness and performance gains of the proposed algorithm.

140 Variational Bayesian Super Resolution

In this paper, we address the super resolution (SR) problem froma set of degraded lowresolution (LR) images to obtain a
high resolution (HR) image. Accurate estimation of the sub-pixel motion between theLRimages significantly affects the
performance of the reconstructedHRimage. In thispaper,weproposenovel super resolution methods where theHRimage and
the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image,
the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a
variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns.
The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the
algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust
to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms
simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are
fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a
stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the
proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.

141 ViBe: A Universal Background Subtraction Algorithm for Video Sequences

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our
proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It
then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and
adapts the model by choosing randomly which values to substitute from the background model. This approach differs from
those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be
part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method
in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction
techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of
both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to
the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version


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of our algorithm performs better than mainstream techniques.

142 Video Tracking Based on Sequential Particle Filtering on Graphs

In this paper, we develop a novel solution for particle filtering on general graphs. We provide an exact solution for particle
filtering on directed cycle-free graphs. The proposed approach relies on a partial-order relation in an antichain
decomposition that forms a high-order Markov chain over the partitioned graph. We subsequently derive a closed-form
sequential updating scheme for conditional density propagation using particle filtering on directed cycle-free graphs.We
also provide an approximate solution for particle filtering on general graphs by splitting graphs with cycles into multiple
directed cycle-free subgraphs. We then use the sequential updating scheme by alternating among the directed cycle-free
subgraphs to obtain an estimate of the density propagation.We rely on the proposed method for particle filtering on general
graphs for two video tracking applications: 1) object tracking using high-orderMarkov chains; and 2) distributed multiple
object tracking based on multi-object graphical interaction models. Experimental results demonstrate the improved
performance of the proposed approach to particle filtering on graphs compared with existing methods for video tracking.

143 Window-Level Rate Control for Smooth Picture Quality and Smooth Buffer Occupancy

In rate control, smooth picture quality and smooth buffer occupancy are both important but contrary to each other at a
given bit rate. How to get a good tradeoff between them was not devoted much attention previously. To deal with this
problem, a theoretical window model is proposed in this paper, in which several adjacent frames grouped as a window are
considered together. The smoothness of both picture quality and buffer occupancy can be gracefully achieved by
regulating the size of the window. To illustrate the usage of window model, a window-level rate control algorithm
cooperated with the traditional -domain rate-distortion model is further introduced. In experiments, we first show howthe
proposed windowmodel achieves the tradeoff between picture quality smoothness and buffer smoothness, and then
demonstrate the significant PSNR improvement, accuracy of bit control and consistency of visual quality of the proposed
window-level rate control algorithm.


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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Imageprocessing

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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Imageprocessing