Published on

final year projects MANGALORE,live projects in MANGALORE, Academic projects in MANGALORE, mini projects in MANGALORE, M.Tech projects in MANGALORE, B.Tech projects in MANGALORE, 8051 PROJECTS, M.S projects in MANGALORE, AVR MICROCONTROLLER PROJECTS, M.C.A projects in MANGALORE, ARM BASED PROJECTS, embedded projects in MANGALORE, INDUSTRIAL BASED PROJECTS, DSP/DIP projects in MANGALORE, ROBOTICS BASED PROJECTS, VLSI projects in MANGALORE, ELECTRONICS FINAL YEAR PROJECTS, Simulation projects in MANGALORE, DIPLOMA BASED FINAL YEAR PROJECTS, .Net projects in MANGALORE,IEEE Projects for Final Year,BE ,M.TECH projects, Final Year Projects Final year Projects , IEEE projects 2013-2014 , IEEE Projects, IEEE Projects 2013 , IEEE Software Projects , IEEE Embedded Projects, BE Projects , M.Tech Projects , MCA Projects , IEEE Power Electroncis Projects , IEEE Power System Projects , NS2 Projects , IEEE Projects MANGALORE , Final Year Projects MANGALORE , IEEE Projects , IEEE Projects 2013 , IEEE Projects 2013 , IEEE Project , Final year Projects , Real Time Projects , Ns2 Projects , ieee projects,ieee projects 2013,ieee Projects 2013-14,2013 ieee projects,ieee 2013 projects,ieee projects 2013,NS2 Projects,IEEE 2013 Projects,ieee projects at MANGALORE,ieee projects 2013 at MANGALORE,ieee projects in MANGALORE,ieee projects 2013 in MANGALORE,MANGALORE ieee projects,ieee projects for cse,,ieee projects for cse 2013,ieee projects 2013 topics, ieee projects 2013 list, ieee projects 2013 for it, ieee projects 2013 for mca, ieee projects 2013 for computer science, ieee projects on networking and network security,ieee projects on cloud computing,ieee projects 2013 in data mining,ieee projects 2013 on image processing,ieee projects 2013 for cse in java,Latest IEEE Projects,IEEE Student Projects, IEEE Final year Student Projects,Final Year Projects,Embedded MTech Projects, Embedded IEEE Projects, IEEE Embedded Projects, Embedded MS Projects, Embedded BTech Projects, Embedded BE Projects, Embedded ME Projects, Embedded IEEE Projects, Embedded IEEE Basepapers, Embedded Final Year Projects, Embedded Ac,BEST EMBEDDED PROJECTS AND MODULES IN MANGALORE,Software Developement,Server Configuration,Final Year Projects,ieee projects,ieee projects 2013,ieee Projects 2013-14,2013 ieee projects,ieee 2013 projects,ieee projects 2013,NS2 Projects,IEEE 2013 Projects,ieee projects at chennai,ieee projects 2013 at chennai,ieee projects in chennai,ieee projects 2013 in chennai,chennai ieee projects,ieee projects for cse,,ieee projects for cse 2013,ieee projects 2013 topics, ieee projects 2013 list, ieee projects 2013 for it, ieee projects 2013 for mca, ieee projects 2013 for computer science, ieee projects on networking and network security,ieee projects on cloud computing,ieee projects 2013 in data mining,ieee projects 2013 on image processing,ieee projects 2013 for cse in java,Latest IEEE Projects,IEEE Student Projects, IEEE Final year Student Projects,Final Year Projects, diploma proj

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. VenSoft Technologies Contact: 9448847874 IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2013-2014 FOR M.Tech/ B.E/B.Tech 1. A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information Abstract: Automatic image registration is a vital yet challenging task, particularly for remote sensing images. A fully automatic registration approach which is accurate, robust, and fast is required. For this purpose, anovel coarse-to-fine scheme for automatic image registration is proposed in this paper. This scheme consists of a preregistration process (coarse registration) and a fine-tuning process (fine registration).To begins with, the preregistration process is implemented by the scale-invariant feature transform approach equipped with a reliable outlier removal procedure. The coarse results provide a near-optimal initial solution for the optimizer in the fine-tuning process. Next, the fine-tuning process is implemented by the maximization of mutual information using a modified Marquardt– Levenberg search strategy in a multi resolution framework. The proposed algorithm is tested on various remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multi sensor, and multi temporal) with the affine transformation model. The experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm. Published in: Geo science and Remote Sensing, IEEE Transactions on (Volume: PP , Issue: 99 ) Index Terms— Image registration, mutual information (MI),outlier removal, scale-invariant feature transform (SIFT). 2. Image Segmentation Using a Sparse Coding Model of Cortical Area V1 Abstract: Algorithms that encode images using a sparse set of basis functions have previously been shown to explain aspects of the physiology of a primary visual cortex (V1), and have been used for applications, such as image compression, restoration, and classification. Here, a sparse coding algorithm, that has previously been used to account for the response properties of orientation tuned cells in primary visual cortex, is applied to the task of perceptually salient boundary detection. The proposed algorithm is currently limited to using only intensity information at a single scale. However, it is shown to out-perform the current state-of-the-art image segmentation method (Pb) when this method is also restricted to using the same information. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 ) Index Terms— Image Segmentation; Edge detection; Neural Networks; Predictive Coding; Sparse Coding; Primary Visual Cortex VenSoft Technologies Contact: 9448847874
  2. 2. VenSoft Technologies Contact: 9448847874 3. How to SAIF-ly Boost Denoising Performance Abstract: Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression kernel) have achieved great success in de noising. Their overall performance, however, has not generally surpassed the leading transform domain-based filters (such as BM3D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune their de noising strength; something that is relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In this paper, we propose spatially adaptive iterative filtering (SAIF) a new strategy to control the de noising strength locally for any spatial domain method. This approach is capable of filtering local image content iteratively using the given base filter, and the type of iteration and the iteration number are automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the oftenemployed SURE method, and exceeds its performance in many cases. Experiments illustrate that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters, and effectively boost the performance of several existing de noising filters to generate state-of-the-art results under both simulated and practical conditions. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 ) Index Terms— Image de noising, spatial domain filter, risk estimator, SURE, pixel aggregation 4. Nonlocally Centralized Sparse Representation for Image Restoration Abstract: Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration, in this paper the concept of sparse coding noise is introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original image, and then centralize the sparse coding coefficients of the observed image to those estimates. The so-called non locally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on VenSoft Technologies Contact: 9448847874
  3. 3. VenSoft Technologies Contact: 9448847874 various types of image restoration problems, including de noising, de blurring and superresolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 ) Date of Publication: April 2013 Index Terms— Image restoration, nonlocal similarity, sparse representation. 5. Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling Abstract: Sparse representation is proven to be a promising approach to image superresolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal selfsimilarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARMbased image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 ) Date of Publication: April 2013 Index Terms—Image interpolation, nonlocal autoregressive model, sparse representation, super-resolution. 6. Acceleration of the Shiftable Algorithm for Bilateral Filtering and Nonlocal Means Abstract: A direct implementation of the bilateral filter requires O(σs2) operations per pixel, where σs is the(effective) width of the spatial kernel. A fast VenSoft Technologies Contact: 9448847874
  4. 4. VenSoft Technologies Contact: 9448847874 implementation of the bilateral filter that required O(1) operations per pixel with respect to σs was recently proposed. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shift ability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated that this idea could be extended to a larger class of filters, including the popular non-local means filter. As already observed, a flip side of this approach was that the run time depended on the width σr of the range kernel. For an image with dynamic range [0,T], the run time scaled as O(T2/σr2) with σr. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. In this paper, we discuss this problem, and propose some simple steps to accelerate the implementation, in general, and for small σr in particular. We provide some experimental results to demonstrate the acceleration that is achieved using these modifications. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 ) Date of Publication: April 2013 Index Terms—Bilateral filter, non-local means, shiftability, constant-time algorithm, Gaussian kernel, truncation, running maximum, max filter, recursive filter, O(1) complexity. 7. Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking Abstract: Visual tracking usually requires an object appearance model that is robust to changing illumination, pose, and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object appearance model is built. This approach has the following limitations: 1) The bases are data driven, so they can be easily corrupted, and 2) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these highfrequency coefficients, we simultaneously obtain a compact 3D-DCT-based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCTalgorithm which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1DDCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to VenSoft Technologies Contact: 9448847874
  5. 5. VenSoft Technologies Contact: 9448847874 compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational complexity. Based on this incremental 3DDCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object. We then embed the discriminative criterion into a particle filtering framework for object state inference over time. Experimental results demonstrate the effectiveness and robustness of the proposed tracker. Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 4 ) Date of Publication: April 2013 Index Terms—Visual tracking, appearance model, compact representation, discrete cosine transform (DCT), incremental learning, template matching. 8 . Visual Saliency Based on Scale-Space Analysis in the Frequency Domain Abstract: We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of non saliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a lowpass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention. Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 4 ) Date of Publication: April 2013 Index Terms—Visual attention, saliency, Hypercomplex Fourier Transform, eye-tracking, scale space analysis. VenSoft Technologies Contact: 9448847874
  6. 6. VenSoft Technologies Contact: 9448847874 9. Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum Abstract: With the wide applications of saliency information in visual signal processing, many saliency detection methods have been proposed. However, some key characteristics of the human visual system (HVS) are still neglected in building these saliency detection models. In this paper, we propose a new saliencydetection model based on the human visual sensitivity and the amplitude spectrum of quaternion Fourier transform (QFT). We use the amplitude spectrum of QFT to represent the color, intensity, and orientation distributions for image patches. The saliency value for each image patch is calculated by not only the differences between the QFT amplitude spectrum of this patch and other patches in the whole image, but also the visual impacts for these differences determined by the human visual sensitivity. The experiment results show that the proposed saliency detection model outperforms the state-of-the-art detection models. In addition, we apply our proposed model in the application of image retargeting and achieve better performance over the conventional algorithms. Published in: Multimedia, IEEE Transactions on (Volume:14 , Issue: 1 ) Date of Publication: Feb. 2012 Index Terms—Amplitude spectrum, Fourier transform, human visual sensitivity, saliency detection, visual attention. 10. Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition Abstract: Local-feature-based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation, and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation-based local feature methods. The extensive VenSoft Technologies Contact: 9448847874
  7. 7. VenSoft Technologies Contact: 9448847874 FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-the-artlocal-feature-based FR methods. Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue: 6) Biometrics Compendium, IEEE Date of Publication: Dec. 2012 Index Terms—Face recognition, Gabor filtering, LBP, monogenic binary coding, monogenic signal analysis. 11. Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma Demultiplexing and Noise Level Estimation Abstract: This paper adapts the least-squares luma-chroma de multiplexing (LSLCD) de mosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to de mosaic the noisy data. The noise-adaptive de mosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1 ) Date of Publication: Jan. 2013 Index Terms—color filter array, Bayer sampling, demosaicking, noise estimation, noise reduction, noise model 12. Fuzzy Clustering with Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images Abstract: In this paper, we put forward a novel approach for change detection in synthetic aperture radar (SAR)images. The approach classifies changed and unchanged regions by fuzzy c-means (FCM) clustering with a novel Markov random field (MRF) energy function. In order to reduce the effect of speckle noise, a VenSoft Technologies Contact: 9448847874
  8. 8. VenSoft Technologies Contact: 9448847874 novel form of MRF energy function with an additional term is established to modify the membership of each pixel. And the degree of modification is determined by the relationship of the neighborhood pixels. The specific form of the additional term is contingent on different situations, and is established ultimately by utilizing the least square method. Our contributions lie in two aspects. Firstly, in order to reduce the effect of speckle noise, the proposed approach focuses on modifying the membership instead of modifying the objective function. It is computational simple in all the steps involved. Its objective function can just return to the original form of FCM, which leads to its less time consumption than that of some recently improved FCM algorithms obviously. Secondly, the proposed approach modifies the membership of each pixel according to a novel form of MRF energy function through which the neighbors of each pixel as well as their relationship are concerned with. Theoretical analysis and experimental results on real SAR datasets show that the proposed approach can detect the realchanges as well as mitigate the effect of speckle noises. Theoretical analysis and experiments also demonstrate its low time complexity. Published in: Fuzzy Systems, IEEE Transactions on (Volume: PP , Issue: 99 ) Date of Publication : 26 February 2013 Index Terms— Fuzzy clustering, image change detection, synthetic aperture radar, Markov random field. 13. Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation Abstract: In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective andefficient, and is relatively independent of this type of noise. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 2 ) VenSoft Technologies Contact: 9448847874
  9. 9. VenSoft Technologies Contact: 9448847874 Date of Publication: Feb. 2013 Index Terms—Fuzzy clustering, gray-level constraint, image segmentation, kernel metric, spatial constraint. 14. Re initialization-Free Level Set Evolution via Reaction Diffusion Abstract: This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly re initialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly re initialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary anti leakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1 Date of Publication: Jan. 2013 Index Terms—Active contours, image segmentation, level set, partial differential equation (PDE), reaction-diffusion, variational method. 15. Online Object Tracking With Sparse Prototypes Abstract: Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l1 regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image VenSoft Technologies Contact: 9448847874
  10. 10. VenSoft Technologies Contact: 9448847874 observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1 ) Date of Publication: Jan. 2013 Index Terms—Appearance model, _1 minimization, object tracking, principal component analysis (PCA), sparse prototypes 16. Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption Abstract: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content's confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods, such as for PSNR=40 dB. Published in: Information Forensics and Security, IEEE Transactions on (Volume:8 , Issue: 3) Date of Publication: March 2013 Index Terms— Reversible data hiding, image encryption, privacy protection, histogram shift. 17. Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization Abstract: Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approximation problem. Since the rank operator is non convex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation. One major limitation of the existing approaches based on nuclear norm minimization is that all the singular values are VenSoft Technologies Contact: 9448847874
  11. 11. VenSoft Technologies Contact: 9448847874 simultaneously minimized, and thus the rank may not be well approximated in practice. In this paper, we propose to achieve a better approximation to the rank of matrix by truncatednuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values. In addition, we develop a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm. We further develop three efficient iterative procedures, TNNR-ADMM, TNNR-APGL, and TNNR-ADMMAP, to solve the optimization problem. TNNR-ADMM utilizes the alternating direction method of multipliers (ADMM), while TNNR-AGPL applies the accelerated proximal gradient line search method (APGL) for the final optimization. For TNNR-ADMMAP, we make use of an adaptive penalty according to a novel update rule for ADMM to achieve a faster convergence rate. Our empirical study shows encouraging results of the proposed algorithms in comparison to the state-of-theart matrix completion algorithms on both synthetic and real visual datasets. Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 9 ) Date of Publication: Sept. 2013 Index Terms—Matrix completion, nuclear norm minimization, alternating direction method of multipliers, accelerated proximal gradient Method 18. Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts Abstract: In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the de mosaicking algorithm. The proposed method is based on a new feature measuring the presence of de mosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different de mosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our scheme. Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue: 5 ) Date of Publication: Oct. 2012 Index Terms—Image forensics, CFA artifacts, digital camera demosaicing, tampering probability map, forgery localization. VenSoft Technologies Contact: 9448847874