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.


12



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


13



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


14



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


16



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


17



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

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

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