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IEEE 2014 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data

Abstract—Sparse unmixing assumes that each observed signature of a hyper spectral image is a linear combination of only a few spectra (end members) in an available spectral library. It then estimates the fractional abundances of these end members in the scene. The sparse un mixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel algorithm termed as the regularized simultaneous forward–backward greedy algorithm (RSFoBa) for sparse un mixing of hyper spectral data. The RSFoBa has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyper spectral data. In addition, the combination of the forward greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyper spectral image is considered to make the algorithm more effective. We also show that the sublibrary obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithm.

Published in: Geo science and Remote Sensing, IEEE Transactions on (Volume:52 , Issue: 9 )Date of Publication: Sept. 2014Index Terms—Dictionary pruning, greedy algorithm (GA), hyperspectral unmixing, multiple-measurement vector (MMV), sparse unmixing.

2. Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization

Abstract: Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixednoise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparsenonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.

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Published in: Image Processing, IEEE Transactions on (Volume:23 , Issue: 6 )Date of Publication: June 2014Index Terms—Mixed noise removal, weighted encoding, nonlocal, sparse representation.

3. Subspace Matching Pursuit for Sparse Unmixing of Hyperspectral Data

Abstract : Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be expressed as a linear combination of only a few spectra (end members) in a spectral library, known a priori. It then aims at estimating the fractional abundances of these endmembers in the scene. Unfortunately, because of the usually high correlation of the spectral library, the sparse unmixing problem still remains a great challenge. Moreover, most related work focuses on the l1 convex relaxation methods, and little attention has been paid to the use of simultaneous sparse representation via greedy algorithms (GAs) (SGA) for sparse unmixing. SGA has advantages such as that it can get an approximate solution for the l0 problem directly without smoothing the penalty term in a low computational complexity as well as exploit the spatial information of the hyperspectral data. Thus, it is necessary to explore the potential of using such algorithms for sparse unmixing. Inspired by the existing SGA methods, this paper presents a novel GA termed subspace matching pursuit (SMP) forsparse unmixing of hyperspectral data. SMP makes use of the low-degree mixed pixels in thehyperspectral image to iteratively find a subspace to reconstruct the hyperspectral data. It is proved that, under certain conditions, SMP can recover the optimal endmembers from the spectral library. Moreover, SMP can serve as a dictionary pruning algorithm. Thus, it can boost other sparse unmixing algorithms, making them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed algorithm.

Published in: Geoscience and Remote Sensing, IEEE Transactions on (Volume:52 , Issue: 6 )Date of Publication: June 2014Index Terms—Dictionary pruning, greedy algorithm (GA), hyperspectral unmixing, multiple-measurement vector (MMV), simultaneous sparse representation, sparse unmixing, subspace matching pursuit (SMP).

4. Sparse Unmixing of Hyperspectral Data Using Spectral a Priori Information

Abstract: Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral apriori information (SUnSPI), to solve the model. Experimental

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results on both synthetic and real datademonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation.

Published in: Geoscience and Remote Sensing, IEEE Transactions on (Volume:53 , Issue: 2 )Date of Publication: Feb. 2015Index Terms—Hyperspectral unmixing, sparse unmixing, alternating direction method of multipliers (ADMM), spectral a priori information.

5. Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising

Abstract: Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknownimage. Our experimental results demonstrate that the proposed GHP algorithm can well preserve thetexture appearance in the denoised images, making them look more natural.

Published in: Image Processing, IEEE Transactions on (Volume:23 , Issue: 6 )Date of Publication: June 2014Index Terms—Image denoising, histogram specification, nonlocal similarity, sparse representation.

6. Image Set based Collaborative Representation for Face Recognition

Abstract: With the rapid development of digital imaging and communication technologies, image set-based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set using the gallery face image sets. Theset-to-set distance-based methods ignore the relationship between gallery sets, whereas representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set-based collaborative representationand classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representationcoefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally

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and effectively extends the image-based collaborativerepresentation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.

Published in: Information Forensics and Security, IEEE Transactions on (Volume:9 , Issue: 7 )Date of Publication: July 2014Index Terms—image set, collaborative representation, set to sets distance, face recognition

7. Fast Compressive Tracking

Abstract : It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multis cale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:PP , Issue: 99 ) April 2014Index Terms—Visual Tracking, Random Projection, Compressive Sensing, Compressed sensing, Feature extraction, Image coding, Object tracking, Robustness, Sparse matrices, Target tracking

8. Speech Intelligibility Prediction Based on Mutual Information

Abstract: This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple

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function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting.

Published in: Audio, Speech, and Language Processing, IEEE/ACM Transactions on (Volume:22 ,Issue: 2 )Date of Publication: Feb. 2014Index Terms— Instrumental measures, noise reduction, objective distortion measures, speech enhancement, speech intelligibility prediction.

9. Super-Resolution Compressed Sensing: An Iterative Reweighted Algorithm for Joint Parameter Learning and Sparse Signal Recovery

Abstract : In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and thesparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweavediterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

Published in: Signal Processing Letters, IEEE (Volume:21 , Issue: 6 )Date of Publication: June 2014Index Terms—Compressed sensing, super-resolution, parameter learning, sparse signal recovery

10. Variants of non-negative least-mean-square algorithm and convergence analysis

Abstract: Due to the inherent physical characteristics of systems under investigation, non-negativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. TheNon-Negative Least-Mean-Square algorithm (NNLMS) was proposed to adaptively find solutions of a typical Wiener filtering problem but with the side constraint that the resulting weights need to be non-negative. It has been shown to have good convergence properties. Nevertheless, certain practical applications may benefit from the use of modified versions of this algorithm. In this paper, we derive three variants of NNLMS. Each variant aims at improving the NNLMS performance regarding one of the following aspects: sensitivity of input power, unbalance of convergence rates for different weights and

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computational cost. We study the stochastic behavior of the adaptive weights for these three new algorithms for non-stationary environments. This study leads to analytical models to predict the first and second order moment behaviors of the weights for Gaussian inputs. Simulation results are presented to illustrate the performance of the new algorithms and the accuracy of the derived models.

Published in: Signal Processing, IEEE Transactions on (Volume:62 , Issue: 15 )Date of Publication: Aug.1, 2014Keywords: Adaptive signal processing, convergence analysis, exponential algorithm, least-mean-square algorithms, non-negativity constraints, normalized algorithm, sign-sign algorithm.

11. Training-Free Non-Intrusive Load Monitoring of Electric Vehicle Charging with Low Sampling Rate

Abstract—Non-intrusive load monitoring (NILM) is an important topic in smart-grid and smart-home. Many energy disaggregation algorithms have been proposed to detect various individual appliances from one aggregated signal observation. However, few works studied the energy disaggregation of plugin electric vehicle (EV) charging in the residential environment since EVs charging at home has emerged only recently. Recent studies showed that EV charging has a large impact on smartgrid especially in summer. Therefore, EV charging monitoring has become a more important and urgent missing piece in energy disaggregation. In this paper, we present a novel method to disaggregate EV charging signals from aggregated real power signals. The proposed method can effectively mitigate interference coming from air-conditioner (AC), enabling accurate EV charging detection and energy estimation under the presence of AC power signals. Besides, the proposed algorithm requires no training, demands a light computational load, delivers high estimation accuracy, and works well for data recorded at the low sampling rate 1/60 Hz. When the algorithm is tested on real-world data recorded from 11 houses over about a whole year (total 125 months worth of data), the averaged error in estimating energy consumption of EV charging is 15.7 kwh/month (while the true averaged energy consumption of EV charging is 208.5 kwh/month), and the averaged normalized mean square error in disaggregating EV charging load signals is 0.19.

Keywords—Non-intrusive load monitoring (NILM); Electric Vehicle (EV); Smart Grid; Energy Disaggregation

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IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. 2-Dimensional Wavelet Packet Spectrum for Texture Analysis

Abstract :This brief derives a 2-D spectrum estimator from some recent results on the statistical properties ofwavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. This brief also discusses the performance of this wavelet-based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator on textureanalysis and content-based image retrieval. It highlights the effectiveness of the wavelet-basedspectrum estimation.

Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 6 )Date of Publication: June 2013Keywords – 2-D Wavelet packet transforms; Random fields; Spectral analysis, Spectrum estimation, Similarity measurements.

2. Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

Abstract—Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is that for each noise type a model is required to be trained a priori. In this paper, we investigate a new class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF). To circumvent the mismatch problem between the training and testing stages, we propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean square error (MMSE) estimator for the speech signal with no information about the underlying noise type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used to develop an unsupervised speech enhancement system. Extensive experiments are carried out to investigate the performance of the proposed methods under different conditions. Moreover, we compare the performance of the developed algorithms with state-of-the-art speech enhancement schemes using various objective measures. Our simulations show that the proposed BNMF-based methods outperform the competing algorithms substantially.

Published in: Audio, Speech, and Language Processing, IEEE Transactions on (Volume:21 , Issue: 10 )Date of Publication: Oct. 2013Index Terms—Nonnegative matrix factorization (NMF), speech enhancement, PLCA, HMM, Bayesian Inference

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3. 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

4. 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 BM3-D). 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 often-employed 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

5. Nonlocally Centralized Sparse Representation for Image Restoration

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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 various types of image restoration problems, including de noising, de blurring and super-resolution, 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 2013Index Terms— Image restoration, nonlocal similarity, sparse representation.

6. Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling

Abstract: Sparse representation is proven to be a promising approach to image super-resolution, 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 self-similarity 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 NARM-based 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 2013Index Terms—Image interpolation, nonlocal autoregressive model, sparse representation, super-resolution.

7. Acceleration of the Shiftable Algorithm for Bilateral Filtering and Nonlocal Means

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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 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/σr

2) 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 2013Index Terms—Bilateral filter, non-local means, shiftability, constant-time algorithm, Gaussian kernel, truncation, running maximum, max filter, recursive filter, O(1) complexity.

8. 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 high-frequency 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 (1D-DCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to 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 3D-DCT 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 )

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Date of Publication: April 2013Index Terms—Visual tracking, appearance model, compact representation, discrete cosine transform (DCT), incremental learning, template matching.

9 . 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 low-pass 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 2013Index Terms—Visual attention, saliency, Hypercomplex Fourier Transform, eye-tracking, scale space analysis.

10. 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. 2013Index Terms—color filter array, Bayer sampling, demosaicking, noise estimation, noise reduction, noise model

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11. 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 )Date of Publication: Feb. 2013Index Terms—Fuzzy clustering, gray-level constraint, image segmentation, kernel metric, spatial constraint.

12. 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.

13. Online Object Tracking With Sparse Prototypes

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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 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. 2013Index Terms—Appearance model, _1 minimization, object tracking, principal component analysis (PCA), sparse prototypes

14. 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 2013Index Terms— Reversible data hiding, image encryption, privacy protection, histogram shift.

15. 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 simultaneously minimized, and thus the rank may not be well approximated in

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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-the-art 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. 2013Index Terms—Matrix completion, nuclear norm minimization, alternating direction method of multipliers, accelerated proximal gradient Method

16. A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform

Abstract: Researchers have been taking advantage of visual attention in various image processing applications such as image retargeting, video coding, etc. Recently, many saliency detection algorithms have been proposed by extracting features in spatial or transform domains. In this paper, a novel saliency detection model is introduced by utilizing low-level features obtained from the wavelet transform domain. Firstly, wavelet transform is employed to create the multi-scale feature maps which can represent different features from edge to texture. Then, we propose a computational model for the saliency map from these features. The proposed model aims to modulate local contrast at a location with its global saliency computed based on the likelihood of the features, and the proposed model considers local center-surround differences and global contrast in the final saliency map. Experimental evaluation depicts the promising results from the proposed model by outperforming the relevant state of the artsaliency detection models.

Published in: Multimedia, IEEE Transactions on (Volume:15 , Issue: 1 )Date of Publication: Jan. 2013Index Terms—Feature map, saliency detection, saliency map, visual attention, wavelet transform.

17. Robust Point Matching Revisited: A Concave Optimization Approach

Abstract- The well-known robust point matching (RPM) method uses deterministic annealing for optimization, and it has two problems. First, it cannot guarantee the global optimality of the solution and tends to align the centers of two point sets. Second, deformation needs to be reg- ularized to avoid the generation of undesirable results. To address these problems, in this paper we show that the energy function of RPM can be reduced to a concave function with very few non-rigid terms after eliminating the transformation variables and applying linear transformation; we then propose to use concave

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optimization technique to minimize the resulting energy function. The proposed method scales well with problem size, achieves the globally optimal solution, and does not need regularization for simple transformations such as similarity transform. Experiments on synthetic and real data validate the advantages of our method in comparison with state-of-the-art methods.

18. Phase Noise in MIMO Systems: Bayesian Cram´er-Rao Bounds and Soft-Input Estimation

Abstract: This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation. New exact and closed-form expressions for the Bayesian Cramer-Rao lower bounds (BCRLBs) and soft-input maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' mean-square error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame, a novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use soft statistics of the transmitted symbols given the current observations are proposed. Numerical results indicate that by employing the proposed phase tracking approach, the bit-error rate performance of a MIMO system affected by phase noise can be significantly improved. In addition, simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in MIMO systems that employ imperfect oscillators.

Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 10 )Issue Date : May15, 2013Index Terms—Multi-input multi-output (MIMO), Wiener phase noise, Bayesian Cram´er Rao lower bound (BCRLB), maximum-a-posteriori (MAP), soft-decision extended Kalman filter (EKF), and extended Kalman smoother (EKS).

19. Multi scale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks

Abstract: This paper describes and analyzes a hierarchical algorithm called Multi scale Gossip for solving the distributed average consensus problem in wireless sensor networks. The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest scale gossip to compute local averages. Then, using multi-hop communication and geographic routing to communicate between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed.

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We show that the proposed hierarchical scheme with k=Θ(loglogn) levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms in terms of message complexity, achieving ε-accuracy with high probability after O(n loglogn log[1/(ε)] ) single-hop messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the above scaling law is achieved when sub networks at scale j contain O(n(2/3)j) nodes; then the message complexity at any individual scale is O(n log[1/ε]). Another important consequence of the hierarchical construction is that the longest distance over which messages are exchanged is O(n1/3) hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms based on averaging along paths.

Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 9 )Date of Publication: May1, 2013

20. Compressed Sensing of EEG for Wireless Tele monitoring with Low Energy Consumption and Inexpensive Hardware

Abstract: Tele monitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non sparse in the time domain and also non sparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the tele monitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for tele monitoring of EEG and other non sparse physiological signals.

Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 1 )Date of Publication: Jan. 2013Index Terms—Telemonitoring, Healthcare, Wireless Body- Area Network (WBAN), Compressed Sensing (CS), Block Sparse Bayesian Learning (BSBL), electroencephalogram (EEG)

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21. Compressed Sensing for Energy-Efficient Wireless Tele monitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning

Abstract: Fetal ECG (FECG) tele monitoring is an important branch in telemedicine. The design of a tele monitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as non sparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct non sparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 2 )Date of Publication: Feb. 2013Index Terms—Fetal ECG (FECG), Tele monitoring, Telemedicine, Healthcare, Block Sparse Bayesian Learning (BSBL), Compressed Sensing (CS), Independent Component Analysis (ICA)

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IEEE 2012 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. 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. 2012Index Terms—Image forensics, CFA artifacts, digital camera demosaicing, tampering probability map, forgery localization.

2. 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. 2012Index Terms—Amplitude spectrum, Fourier transform, human visual sensitivity, saliency detection, visual attention.

3. 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

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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 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, IEEEDate of Publication: Dec. 2012Index Terms—Face recognition, Gabor filtering, LBP, monogenic binary coding, monogenic signal analysis.

4. A Joint Time-Invariant Filtering Approach to the Linear Gaussian Relay Problem

Abstract :In this paper, the linear Gaussian relay problem is considered. Under the linear time-invariant (LTI) model the rate maximization problem in the linear Gaussian relay channel is formulated in the frequency domain based on the Toeplitz distribution theorem. Under the further assumption of realizable input spectra, the rate maximization problem is converted to the problem of joint source and relay filter design with two power constraints, one at the source and the other at the relay, and a practical solution to this problem is proposed based on the (adaptive) projected (sub)gradient method. Numerical results show that the proposed method yields a considerable gain over the instantaneous amplify-and-forward (AF) scheme in inter-symbol interference (ISI) channels. Also, the optimality of the AF scheme within the class of one-tap relay filters is established in flat-fading channels.

Published in: Signal Processing, IEEE Transactions on (Volume:60 , Issue: 8 )Date of Publication: Aug. 2012Index Terms—Filter design, linear Gaussian relay, linear time invariant model, projected subgradient method, Toeplitz distribution theorem.

5. Monotonic Regression: A New Way for Correlating Subjective and Objective Ratings in Image Quality Research

Abstract: To assess the performance of image quality metrics (IQMs), some regressions, such as logistic regression and polynomial regression, are used to correlate objective ratings with subjective scores. However, some defects in optimality are shown in these regressions. In this correspondence, monotonic regression (MR) is found to be an effective correlation method in the performance

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assessment of IQMs. Both theoretical analysis and experimental results have proven that MR performs better than any other regression. We believe that MR could be an effective tool for performance assessment in the IQM research.

Published in: Image Processing, IEEE Transactions on (Volume:21 , Issue: 4 )Date of Publication: April 2012Index Terms—Image quality assessment, image quality metric (IQM), metric performance, monotonic regression (MR).

6. An efficient leaf recognition algorithm for plant classification using support vector machine

Abstract: Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The main contribution of this approach is the SupportVector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.

Published in: Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference onDate of Conference: 21-23 March 2012Keywords- Digital Morphological Features (DMFs); Leaf Recognition; Support Vector Machine

7. Image Signature: Highlighting Sparse Salient Regions

Abstract—We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.

Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:34 , Issue: 1 )Date of Publication: Jan. 2012

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Index Terms—Saliency, visual attention, change blindness, sign function, sparse signal analysis.8. An Efficient Algorithm for Level Set Method Preserving Distance Function

Abstract: The level set method is a popular technique for tracking moving interfaces in several disciplines, including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issues. First, the level set method does not implicitly preserve the level set function as a distance function, which is necessary to estimate accurately geometric features, s.a. the curvature or the contour normal. Second, the level set algorithm is slow because the time step is limited by the standard Courant-Friedrichs-Lewy (CFL) condition, which is also essential to the numerical stability of the iterative scheme. Recent advances with graph cut methods and continuous convex relaxation methods provide powerful alternatives to the level set method for image processing problems because they are fast, accurate, and guaranteed to find the global minimizer independently to the initialization. These recent techniques use binary functions to represent the contour rather than distance functions, which are usually considered for the level set method. However, the binary function cannot provide the distance information, which can be essential for some applications, s.a. the surface reconstruction problem from scattered points and the cortex segmentation problem in medical imaging. In this paper, we propose a fast algorithm to preserve distance functions inlevel set methods. Our algorithm is inspired by recent efficient l1 optimization techniques, which will provide an efficient and easy to implement algorithm. It is interesting to note that our algorithm is not limited by the CFL condition and it naturally preserves the level set function as a distance function during the evolution, which avoids the classical re-distancing problem in level set methods. We apply the proposed algorithm to carry out image segmentation, where our methods prove to be 5-6 times faster than standard distance preserving level set - techniques. We also present two applications where preserving a distance function is essential. Nonetheless, our method stays generic and can be applied to any level set methods that require the distance information.

Published in: Image Processing, IEEE Transactions on (Volume:21 , Issue: 12 )Date of Publication: Dec. 2012Index Terms: Image segmentation, level set, numerical scheme, signed distance function,splitting, surface reconstruction

9. Structure Extraction from Texture via Relative Total Variation

Abstract: It is ubiquitous that meaningful structures are formed by or appear over textured surfaces. Extracting them under the complication of texture patterns, which could be regular, near-regular, or irregular, is very challenging, but of great practical importance. We propose new inherent variation and relative total variation measures, which capture the essential difference of these two types of visual forms, and develop an efficient optimization system to extract main structures. The new variation measures are validated on millions of sample patches. Our approach finds a number of new applications to manipulate, render, and reuse the immense number of “structure with texture” images and drawings that were traditionally difficult to be edited properly.

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Keywords: texture, structure, smoothing, total variation, relative total variation, inherent variation, prior, regularized optimization2012

10. Quick Detection of Brain Tumors and Edemas: A Bounding Box Method Using Symmetry

Abstract: A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging as well as computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices. The output is a corresponding set of the slices that circumscribe the tumors with axis-parallel bounding boxes. The proposed approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52 respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques.

Index Terms– MR image Segmentation, Bhattacharya coefficient, Brain Tumor, Edema.

11. Efficient Misalignment-Robust Representation for Real-Time Face Recognition

Abstract: Sparse representation techniques for robust face recognition have been widely studied in the past several years. Recently face recognition with simultaneous misalignment, occlusion and other variations has achieved interesting results via robust alignment by sparse representation (RASR). In RASR, the best alignment of a testing sample is sought subject by subject in the database. However, such an exhaustive search strategy can make the time complexity of RASR prohibitive in large-scale face databases. In this paper, we propose a novel scheme, namely misalignment robust representation (MRR), by representing the misaligned testing sample in the transformed face space spanned by all subjects. The MRR seeks the best alignment via a two-step optimization with a coarse-to-fine search strategy, which needs only two deformation-recovery operations. Extensive experiments on representative face databases show that MRR has almost the same accuracy as RASR in various face recognition and verification tasks but it runs tens to hundreds of times faster than RASR. The running time of MRR is less than 1 second in the large-scale Multi-PIE face database, demonstrating its great potential for real-time face recognition.

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12. Multi-User Diversity vs. Accurate Channel State Information in MIMO Downlink Channels

Abstract: In a multiple transmit antenna, single antenna per receiver downlink channel with limited channel state feedback, we consider the following question: given a constraint on the total system-wide feedback load, is it preferable to get low-rate/coarse channel feedback from a large number of receivers or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. We show that there is a strong preference for obtaining high-quality feedback, and that obtaining near-perfect channel information from as many receivers as possible provides a significantly larger sum rate than collecting a few feedback bits from a large number of users. In terms of system design, this corresponds to a preference for acquiring high-quality feedback from a few users on each time-frequency resource block, as opposed to coarse feedback from many users on each block.

Published in: Wireless Communications, IEEE Transactions on (Volume:11 , Issue: 9 )Date of Publication: September 2012Index Terms-MIMO downlink channels, MU-MIMO communication, multi-user diversity

13. Joint Estimation of Channel and Oscillator Phase Noise in MIMO SystemsAbstract: Oscillator phase noise limits the performance of high speed communication systems since it results in time varying channels and rotation of the signal constellation from symbol to symbol. In this paper, jointestimation of channel gains and Wiener phase noise in multi-input multi-output (MIMO) systems is analyzed. The signal model for the estimation problem is outlined in detail and new expressions for the Cramer-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. A data-aided least-squares (LS) estimator for jointly obtaining the channel gains and phase noise parameters is derived. Next, a decision-directed weighted least-squares (WLS) estimator is proposed, where pilots and estimated data symbols are employed to track the time-varying phase noise parameters over a frame. In order to reduce the overhead and delay associated with the estimation process, a new decision-directed extended Kalman filter (EKF) is proposed for tracking the MIMO phase noise throughout a frame. Numerical results show that the proposed LS, WLS, and EKF estimators' performances are close to the CRLB. Finally, simulation results demonstrate that by employing the proposed channel and time-varying phase noise estimators the bit-error rate performance of a MIMO system can be significantly improved.

Published in: Signal Processing, IEEE Transactions on (Volume:60 , Issue: 9 )Date of Publication: Sept. 2012Index Terms—Channel estimation, Cramer-Rao lower bound (CRLB), extended Kalman filter (EKF), multi-input multi-output (MIMO), weighted least squares (WLS), Wiener phase noise.

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IEEE 2011 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. An Augmented Lagrangian Method for Total Variation Video Restoration

Abstract: This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total variation regularization to enhance the smoothness of the solution. The optimization problem is solved by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method is used to iteratively find solutions to the subproblems. The proposed algorithm has a wide range of applications, including video deblurring and denoising, video disparity refinement, and hot-air turbulence effect reduction.

Published in: Image Processing, IEEE Transactions on (Volume:20 , Issue: 11 )Date of Publication: Nov. 2011Index Terms: Alternating direction method (ADM), augmented Lagrangian, hot-air turbulence, total variation (TV), video deblurring, video disparity, video restoration

2. On Optimal Power Control for Delay-Constrained Communication Over Fading Channels

In this paper, the problem of optimal power control for delay-constrained communication over fading channels is studied. The objective is to find a power control law that optimizes the link layer performance, specifically, minimizes delay bound violation probability (or equivalently, the packet drop probability), subject to constraints on average power, arrival rate and delay bound. The transmission buffer size is assumed to be finite; hence, when the buffer is full, there will be packet drop. The fading channel under study has a continuous state, e.g., Rayleigh fading. Since directly solving the power control problem (which optimizes the link layer performance) is particularly challenging, the problem is decomposed into three sub problems and the three sub problems are solved iteratively; the resulting scheme is called joint queue length aware (JQLA) power control, which produces a local optimal solution to the three sub problems. It is proved that the solution that simultaneously solves the three sub problems is also an optimal solution to the optimal power control problem. Simulation results show that the JQLA scheme achieves superior performance over the time domain water filling and the truncated channel inversion power control.

Published in: Information Theory, IEEE Transactions on (Volume:57 , Issue: 6 )Date of Publication: June 2011Index Terms—Delay-constrained communication, power control, queuing analysis, delay bound violation probability, packet drop probability.

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3. A Level Set Method for Image Segmentation in the Presence of Intensity InhomogeneitiesWith Application to MRI

Abstract: Intensity in homogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In alevel set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.

Published in: Image Processing, IEEE Transactions on (Volume:20 , Issue: 7 )Date of Publication: July 2011Index Terms: Bias correction, MRI, image segmentation, intensity inhomogeneity, level set

4. Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems

Abstract—In this paper, the results for the CEC 2011 Competition on testing evolutionary algorithms on real world optimization problems using a hybrid differential evolution algorithm are presented. The proposal uses a local search routine to improve convergence and an adaptive crossover operator. According to the obtained results, this algorithm shows to be able to find competitive solutions with reported results.

Index Terms—Differential Evolution algorithm, parameter selection, CEC competition.Published in: Evolutionary Computation (CEC), 2011 IEEE Congress on June 2011

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5. An Improved Algorithm for Blind Reverberation Time Estimation

Abstract—An improved algorithm for the estimation of the reverberation time (RT) from reverberant speech signals is presented. This blind estimation of the RT is based on a simple statistical model for the sound decay such that the RT can be estimated by means of a maximum-likelihood (ML) estimator. The proposed algorithm has a significantly lower computational complexity than previous ML-based algorithms for RT estimation. This is achieved by a downsampling operation and a simple pre-selection of possible sound decays. The new algorithm is more suitable to track time-varying RTs than related approaches. In addition, it can also estimate the RT in the presence of (moderate) background noise. The proposed algorithm can be employed to measure the RT of rooms from sound recordings without using a dedicated measurement setup. Another possible application is its use within speech dereverberation systems for hands-free devices or digital hearing aids.

Index Terms—reverberation time, blind estimation, low complexity, speech dereverberation

IEEE 2010 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. Distance Regularized Level Set Evolution and Its Application to Image Segmentation

Abstract—Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.

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Published in: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 12, DECEMBER 2010Index Terms—Forward and backward diffusion, image segmentation, level set method, narrowband, reinitialization.

2. Demonstration of Real-Time Spectrum Sensing for Cognitive Radio

Abstract: The requirement for real-time processing indeed poses challenges on implementing spectrum sensingalgorithms. Trade-off between the complexity and the effectiveness of spectrum sensing algorithms should be taken into consideration. In this paper, a fast Fourier transform based spectrum sensingalgorithm, whose decision variable is independent of noise level, is introduced. A small form factor software defined radio development platform is employed to implement a spectrum sensing receiver with the proposed algorithm. To our best knowledge, it is the first time that real-time spectrum sensingon hardware platform with controllable primary user devices is demonstrated.

Published in: Communications Letters, IEEE (Volume:14 , Issue: 10 )Date of Publication: October 2010Index Terms: Cognitive radio, demonstration, real-time, spectrum sensing

3. Retinal Vessel Extraction by Matched Filter with First-Order Derivative of Gaussian

Abstract: Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. The Matched Filter (MF) is a simple yet effective method for vessel extraction. However, a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by thresholding the retinal image’s response to the MF, while the threshold is adjusted by the image’s response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly the false detections produced by the original MF and detects many fine vessels that are missed by the MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes but with much lower complexity. In addition, it performs well at extracting vessels from pathological retinal images.

Keywords: retinal image segmentation; vessel detection; matched filter; line detection2010

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4. Accurate Computation of the MGF of the Lognormal Distribution and its Application to Sum of Lognormals

Abstract: Sums of lognormal random variables (RVs) are of wide interest in wireless communications and other areas of science and engineering. Since the distribution of lognormal sums is not log-normal and does not have a closed-form analytical expression, many approximations and bounds have been developed. This paper develops two computational methods for the moment generating function (MGF) or the characteristic function (CHF) of a single lognormal RV. The first method uses classical complex integration techniques based on steepest-descent integration. The saddle point of the integrand is explicitly expressed by the Lambert function. The steepest-descent (optimal) contour and two closely-related closed-form contours are derived. A simple integration rule (e.g., the midpoint rule) along any of these contours computes the MGF/CHF with high accuracy. The second approach uses a variation on the trapezoidal rule due to Ooura and Mori. Importantly, the cumulative distribution function of lognormalsums is derived as an alternating series and convergence acceleration via the Epsilon algorithm is used to reduce, in some cases, the computational load by a factor of 106! Overall, accuracy levels of 13 to 15 significant digits are readily achievable.

Published in: Communications, IEEE Transactions on (Volume:58 , Issue: 5 )Date of Publication: May 2010Index Terms—Sum of lognormals, moment-generating function, characteristic function.

IEEE <2009 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech

1. Canny Edge Detection Enhancement by Scale Multiplication

The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edgedetection performance. Experimental results are presented.

Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:27 , Issue: 9 )Date of Publication: Sept. 2005

2. Performance analysis of channel estimation and adaptive equalization in slow fading channel

ABSTRACT: In our project, we first build up a wireless communication simulator including Gray coding, modulation, different channel models (AWGN, flat fading and frequency selective fading channels), channel estimation, adaptive equalization, and demodulation. Next, we test the effect of different

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channel models to the data and image in receiver with constellation and BER (bit error rate) plots under QPSK modulation. For Image data source, we also compare the received image quality to original image in different channels. At last, we give detail results and analyses of the performance improvement with channel estimation and adaptive equalization in slow Rayleigh fading channel. For frequency selective fading channel, we use linear equalization with both LMS (least mean squares) and RLS (Recursive Least Squares) algorithms to compare the different improvements. We will see that in AWGN channel, the image is degraded by random noise; in flat fading channel, the image is degraded by random noise and block noise; in frequency selective fading channel, the image is degraded by random noise, block noise, and ISI.

Keywords: Slow fading, flat fading, frequency selective fading, channel estimation, LMS, RLS2007

3. ROBUST OBJECT TRACKING USING JOINT COLOR-TEXTURE HISTOGRAM

Abstract: A novel object tracking algorithm is presented in this paper by using the joint color texture histogram to represent a target and then applying it to the mean shift framework. Apart from the conventional color histogram features, the texture features of the object are also extracted by using the local binary pattern (LBP) technique to represent the object. The major uniform LBP patterns are exploited to form a mask for joint color-texture feature selection. Compared with the traditional color histogram based algorithms that use the whole target region for tracking, the proposed algorithm extracts effectively the edge and corner features in the target region, which characterize better and represent more robustly the target. The experimental results validate that the proposed method improves greatly the tracking accuracy and efficiency with fewer mean shift iterations than standard mean shift tracking. It can robustly track the target under complex scenes, such as similar target and background appearance, on which the traditional color based schemes may fail to track.

Keywords: Object tracking; mean shift; local binary pattern; color histogram. 2000

4. Efficient Encoding of Low-Density Parity-Check CodesAbstract—Low-density parity-check (LDPC) codes can be considered serious competitors to turbo codes in terms of performance and complexity and they are based on a similar philosophy: constrained random code ensembles and iterative decoding algorithms. In this paper,we consider the encoding problem for LDPCcodes. More generally, we consider the encoding problem for codes specified by sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to obtain efficient encoders. For the (3 6)-regular LDPC code, for example, the complexity of encoding is essentially quadratic in the block length. However, we showthat the associated coefficient can be made quite small, so that encoding codes even of length 100 000 is still quite practical. More importantly, we will show that “optimized” codes actually admit linear time encoding.

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Published in: Information Theory, IEEE Transactions on (Volume:47 , Issue: 2 )Date of Publication: Feb 2001Index Terms—Binary erasure channel, decoding, encoding, parity check, random graphs, sparse matrices, turbo codes.

5. ML Estimation of Time and Frequency Offset in OFDM Systems

Abstract: We present the joint maximum likelihood (ML) symbol-time and carrier-frequency offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. Redundant information contained within the cyclic prefix enables this estimation without additional pilots. Simulations show that the frequencyestimator may be used in a tracking mode and the time estimator in an acquisition mode

Published in: Signal Processing, IEEE Transactions on (Volume:45 , Issue: 7 )Date of Publication: Jul 1997

Index Terms: OFDM systems, acquisition mode, carrier-frequency offset estimator, cyclic prefix, maximum likelihood, orthogonal frequency-division multiplexing, redundant information, symbol-time estimator, time offset, tracking mode

6. Performance analysis of channel estimation and adaptive equalization in slow fading channel

Abstract: In our project, we first build up a wireless communication simulator including Gray coding, modulation, different channel models (AWGN, flat fading and frequency selective fading channels), channel estimation, adaptive equalization, and demodulation. Next, we test the effect of different channel models to the data and image in receiver with constellation and BER (bit error rate) plots under QPSK modulation. For Image data source, we also compare the received image quality to original image in different channels. At last, we give detail results and analyses of the performance improvement with channel estimation and adaptive equalization in slow Rayleigh fading channel. For frequency selective fading channel, we use linear equalization with both LMS (least mean squares) and RLS (Recursive Least Squares) algorithms to compare the different improvements. We will see that in AWGN channel, the image is degraded by random noise; in flat fading channel, the image is degraded by random noise and block noise; in frequency selective fading channel, the image is degraded by random noise, block noise, and ISI.

Keywords: Slow fading, flat fading, frequency selective fading, channel estimation, LMS, RLS

2007

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7. PARAFAC-Based Blind Estimation Of Possibly Underdetermined Convolutive MIMO Systems

Abstract: In this paper, we consider the problem of blind identification of a convolutive multiple-input-multiple-output (MIMO) system with No outputs and Ni inputs. While many methods have been proposed to blindly identify convolutive MIMO systems with No ges Ni (overdetermined), very scarce results exist for the case of (underdetermined), all of which refer to systems that either have some special structure or special and values. In this paper, we show that, as long as , independent of whether the system is overdetermined or underdetermined, we can always find the appropriate order of statistics that guarantees identifiability of the system response within trivial ambiguities. We also propose an algorithm to reach the solution, that consists of parallel factorization (PARAFAC) of a -way tensor containing th-order statistics of the system outputs, followed by an iterative scheme. For a certain order of statistics , we provide the description of the class of identifiable MIMO systems. We also show that this class can be expanded by applying PARAFAC decomposition to a pair of tensors instead of one tensor. The proposed approach constitutes a novel scheme for estimation of underdetermined systems, and improves over existing approaches for overdetermined systems.

Published in: Signal Processing, IEEE Transactions on (Volume:56 , Issue: 1 )Date of Publication: Jan. 2008Keywords: Blind multiple-input-multiple-output (MIMO), MIMO identification, PARAFAC, higher order statistics, underdetermined MIMO

8. Minimization of Region-Scalable Fitting Energy for Image Segmentation

Abstract—Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.

Index Terms—Image segmentation, intensity inhomogeneity, level set method, region-scalable fitting energy, variational method.Published in: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 10, OCTOBER 2008

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9. Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition

Abstract: This paper addresses subset feature selection performed by the sequential floating forward selection (SFFS). The criterion employed in SFFS is the correct classification rate of the Bayes classifier assuming that the features obey the multivariate Gaussian distribution. A theoretical analysis that models the number of correctly classified utterances as a hypergeometric random variable enables the derivation of an accurate estimate of the variance of the correct classification rate during cross-validation. By employing such variance estimate, we propose a fast SFFS variant. Experimental findings on Danish emotional speech (DES) and Speech Under Simulated and Actual Stress (SUSAS) databases demonstrate that SFFS computational time is reduced by 50% and the correct classification rate for classifying speech into emotional states for the selected subset of features varies less than the correct classification rate found by the standard SFFS. Although the proposed SFFS variant is tested in the framework of speech emotion recognition, the theoretical results are valid for any classifier in the context of any wrapper algorithm.

Key words: Bayes classifier, cross-validation, variance of the correct classification rate of the Bayes classifier, feature selection, wrappers 2008

10. Sum Power Iterative Water-Filling for Multi-Antenna Gaussian Broadcast Channels

Abstract—In this correspondence, we consider the problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC). It was recently found that dirty-paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e., the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty-paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a well-structured convex multiple-access channel (MAC) problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the MAC, which can easily be mapped to the optimal BC policies.

Index Terms—Broadcast channel, dirty-paper coding, duality, multipleaccess channel (MAC), multiple-input multiple-output (MIMO), systems.Published in: IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 4, APRIL 2005

11. Symmetric Capacity of MIMO Downlink ChannelsAbstract: This paper studies the symmetric capacity of the MIMO downlink channel, which is defined to be the maximum rate that can be allocated to every receiver in the system. The symmetric capacity represents absolute fairness and is an important metric for slowly fading channels in which users have symmetric rate demands. An efficient and provably convergent algorithm for computing the symmetric capacity is proposed, and it is shown that a simple modification of the algorithm can be used to compute

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the minimum power required to meet given downlink rate demands. In addition, the difference between the symmetric and sum capacity, termed the fairness penalty, is studied. Exact analytical results for the fairness penalty at high SNR are provided for the 2 user downlink channel, and numerical results are given for channels with more users

Published in: Information Theory, 2006 IEEE International Symposium on July 2006Index Terms: MIMO systems, channel capacity, fading channels, radio links