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Signal Processing Letters, IEEE

Issue 8 • Date Aug. 2014

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Displaying Results 1 - 25 of 36
  • Front Cover

    Page(s): C1
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  • IEEE Signal Processing Letters publication information

    Page(s): C2
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  • Table of Contents

    Page(s): 905 - 906
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  • Table of Contents

    Page(s): 907 - 908
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  • Object Tracking via Robust Multitask Sparse Representation

    Page(s): 909 - 913
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (852 KB) |  | HTML iconHTML  

    Sparse representation has been applied to the object tracking problem. Mining the self-similarities between particles via multitask learning can improve tracking performance. However, some particles may be different from others when they are sampled from a large region. Imposing all particles share the same structure may degrade the results. To overcome this problem, we propose a tracking algorithm based on robust multitask sparse representation (RMTT) in this letter. When we learn the particle representations, we decompose the sparse coefficient matrix into two parts in our algorithm. Joint sparse regularization is imposed on one coefficient matrix while element-wise sparse regularization is imposed on another matrix. The former regularization exploits self-similarities of particles while the later one considers the differences between them. Experiments on the benchmark data show the superior performance over other state-of-art algorithms. View full abstract»

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  • Numerical Synthesis of an Optimal Low-Sidelobe Beam Pattern for a Microphone Array

    Page(s): 914 - 917
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (692 KB) |  | HTML iconHTML  

    This letter describes a numerical algorithm for synthesizing optimal low-sidelobe beampatterns. The pattern synthesis problem is formulated as a constrained optimization that minimizes the spatially weighted energy arriving at the array subject to unit gain in the look direction and a constant sidelobe level. The weighting takes on the form of a sensor correlation matrix, parameterized between uncorrelated sensor noise and correlated signal arrival terms according to a balancing factor. Setting the balancing factor to its extreme values of 0 and 1, produces, respectively, the optimal Riblet-Chebyshev and Delay-and-Sum type beamformers. In between, the method generates a low-sidelobe beamformer with varying beamwidths that provides a trade-off between White Noise Gain and Directivity Index. Simulation examples demonstrate optimal and intermediate designs for uniform and non-uniform sensor spacings. View full abstract»

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  • Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images

    Page(s): 918 - 922
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB) |  | HTML iconHTML  

    Image degradation damages genuine visual structures and causes pseudo structures. Pseudo structures are usually present with regularities. This letter proposes a machine learning based blocking artifacts metric for JPEG images by measuring the regularities of pseudo structures. Image corner, block boundary and color change properties are used to differentiate the blocking artifacts. A support vector regression (SVR) model is adopted to learn the underlying relations between these features and perceived blocking artifacts. The blocking artifacts score of a test image is predicted using the trained model. Extensive experiments demonstrate the effectiveness of the method. View full abstract»

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  • Artifact Removal from Single-Trial ERPs using Non-Gaussian Stochastic Volatility Models and Particle Filter

    Page(s): 923 - 927
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1063 KB) |  | HTML iconHTML  

    This paper considers improved modeling of artifactual noise for denoising of single-trial event-related potentials (ERPs) by state-space approach. Instead of the inadequate constant variance models used in existing studies, we propose to use stochastic volatility (SV) models to better describe the time-varying volatility in real ERP noise sources. We further propose a class of non-Gaussian SV models to capture the abrupt volatility changes typically present in impulsive noise, to improve artifact removal from ERPs. Two specifications are considered: (1) volatility driven by a heavy-tailed component and (2) transformation of volatility. Both result in volatility processes with heavy-tailed transition densities which can predict the impulsive noise volatility dynamics, more accurately than the Gaussian models. These SV noise models are incorporated in an autoregressive (AR) state-space ERP dynamic model. Parameter estimation is done using a Rao-Blackwellized particle filter (RBPF). Evaluation on simulated auditory brainstem responses (ABRs), corrupted by real eye-blink artifacts, shows that the non-Gaussian models can accurately detect the artifact-induced abrupt volatility spikes, and able to uncover the underlying inter-trial dynamics. Among them, the log-SV model performs the best. The results on real data demonstrate significant artifact suppression. View full abstract»

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  • Steady-State Performance of Non-Negative Least-Mean-Square Algorithm and Its Variants

    Page(s): 928 - 932
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1088 KB) |  | HTML iconHTML  

    The Non-Negative Least-Mean-Square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in the literature. In this letter, we derive closed-form expressions for the steady-state excess mean-square error (EMSE) for the four algorithms. Simulation results illustrate the accuracy of the theoretical results. This work complements the understanding of the behavior of these algorithms. View full abstract»

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  • A Time-Frequency Hybrid Downmixing Method for AC-3 Decoding

    Page(s): 933 - 936
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (674 KB) |  | HTML iconHTML  

    In this letter, a time-frequency hybrid downmixing method is proposed for AC-3 decoding. The proposed method consists of downmixing frequency coefficients of long blocks and short blocks respectively, computing long and short inverse modified discrete cosine transforms (IMDCTs), and adding operation. Compared with one reported fast frequency-domain downmixing method, the proposed method does not need frequency coefficients conversion between long and short blocks, and has approximately 26% and 32% reduction in numbers of additions and multiplications, respectively. View full abstract»

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  • Accurate Static Region Classification Using Multiple Cues for ARO Detection

    Page(s): 937 - 941
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (958 KB) |  | HTML iconHTML  

    This letter proposes an accurate static region classification for detecting abandoned or removed objects (ARO) using multiple cues. Most existing ARO detection approaches show many falsely detected static regions and low ARO detection performance in real situations because they use single cue and a number of pre-defined threshold values. The proposed method presents multiple cues as intensity, motion, and shape to characterize the true static regions and classifies their candidates into true/false static regions using a SVM classifier, which avoids any dependency on pre-defined threshold values. Experimental results show that the proposed method achieved better ARO detection accuracy and lower false detection rate than the existing methods. In addition, the proposed method can be utilized to several practical applications such as illegal parking detection, garbage throwing detection, thief detection, forest fire detection, and camouflaged solider detection. View full abstract»

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  • A GMM Post-Filter for Residual Crosstalk Suppression in Blind Source Separation

    Page(s): 942 - 946
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (990 KB) |  | HTML iconHTML  

    Existing algorithms employ the Wiener filter to suppress residual crosstalk in the outputs of blind source separation algorithms. We show that, in the context of BSS, the Wiener filter is optimal in the maximum likelihood (ML) sense only for normally-distributed signals. We then propose to model the distribution of speech signals using the Gaussian mixture model (GMM) and then derive a post-filter in the ML sense using the expectation-maximization algorithm. We show that the GMM introduces a probabilistic sample weight that is able to emphasize speech segments that are free of crosstalk components in the BSS output and this results in a better estimate of the post-filter. Simulation results show that the proposed post-filter achieves better crosstalk suppression than the Wiener filter for BSS. View full abstract»

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  • Robust Transceiver for AF MIMO Relaying with Direct Link: A Globally Optimal Solution

    Page(s): 947 - 951
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1075 KB) |  | HTML iconHTML  

    We study the robust transceiver design for amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems with a direct link and imperfect channel state information (CSI). Under this circumstance, we aim at obtaining the globally optimal transceiver that minimizes the mean-squared error (MSE) of symbol detection. Specifically, given a source beamformer, we first derive closed-form expressions for the optimal relay precoder and destination receiver. Then, to tackle the intricate non-convex problem with respect to source beamformer, we develop an efficient approach involving one-dimensional search and semidefinite programming (SDP). We prove rigourously that the proposed method yields a globally optimal solution. Simulation results verify the pronounced performance provided by the proposed design. View full abstract»

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  • Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images

    Page(s): 952 - 956
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1110 KB) |  | HTML iconHTML  

    In this letter, the classification via sparse representation of the monogenic signal is presented for target recognition in SAR images. To characterize SAR images, which have broad spectral information yet spatial localization, the monogenic signal is performed. Then an augmented monogenic feature vector is generated via uniform down-sampling, normalization and concatenation of the monogenic components. The resulting feature vector is fed into a recently developed framework, i.e., sparse representation based classification (SRC). Specifically, the feature vectors of the training samples are utilized as the basis vectors to code the feature vector of the test sample as a sparse linear combination of them. The representation is obtained via l1-norm minimization, and the inference is reached according to the characteristics of the representation on reconstruction. Extensive experiments on MSTAR database demonstrate that the proposed method is robust towards noise corruption, as well as configuration and depression variations. View full abstract»

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  • Determining the Existence of Objects in an Image and Its Application to Image Thumbnailing

    Page(s): 957 - 961
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1115 KB) |  | HTML iconHTML  

    In recent years, computer vision applications dealing with foreground objects are becoming more important with an increasing demand of advanced intelligent systems. Most of these applications assume that an image contains one or more objects, which often produce undesired results when noticeable objects do not appear in the image. In this letter, we address the problem of ascertaining the existence of objects in an image. In the first step, the input image is partitioned into nonoverlapping local patches, then the patches are categorized into three classes, namely natural, man-made, and object to estimate object candidates. Then a Bayesian methodology is employed to produce more reliable results by eliminating false positives. To boost the object patch detection performance, we exploit the difference between coarse and fine segmentation results. To demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on several benchmark image databases. Furthermore, we have shown the usefulness of our approach by applying it to a real application (i.e., image thumbnailing). View full abstract»

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  • Robust Keypoint Detection Using Higher-Order Scale Space Derivatives: Application to Image Retrieval

    Page(s): 962 - 965
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (539 KB) |  | HTML iconHTML  

    Image retrieval has been extensively studied over the last two decades due to the increasing demands for the effective use of multimedia data. Among various approaches to image retrieval, scale space representation and local keypoint descriptors have been shown to be a promising approach. Even though the concept of scale space representation has been known for a long time, it has now gained prominence as a powerful method for image retrieval mostly due to the invention of the Scale Invariant Feature Transform (SIFT). We will review the characteristics of the scale space operation and provide an extended method of scale space operation that significantly improves the image matching accuracy in the context of image retrieval. We use an operational tattoo image database containing 1,000 near duplicate images to show the superior retrieval performance of the proposed method compared to SIFT keypoints. View full abstract»

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  • Viewpoint-Aware Representation for Sketch-Based 3D Model Retrieval

    Page(s): 966 - 970
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    We study the problem of sketch-based 3D model retrieval, and propose a solution powered by a new query-to-model distance metric and a powerful feature descriptor based on the bag-of-features framework. The main idea of the proposed query-to-model distance metric is to represent a query sketch using a compact set of sample views (called basic views) of each model, and to rank the models in ascending order of the representation errors. To better differentiate between relevant and irrelevant models, the representation is constrained to be essentially a combination of basic views with similar viewpoints. In another aspect, we propose a mid-level descriptor (called BOF-JESC) which robustly characterizes the edge information within junction-centered patches, to extract the salient shape features from sketches or model views. The combination of the query-to-model distance metric and the BOF-JESC descriptor achieves effective results on two latest benchmark datasets. View full abstract»

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  • A Computationally Efficient Subspace Algorithm for 2-D DOA Estimation with L-shaped Array

    Page(s): 971 - 974
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (980 KB) |  | HTML iconHTML  

    In this letter, a computationally efficient subspace algorithm is developed for two-dimensional (2-D) direction-of-arrival (DOA) estimation with L-shaped array structured by two uniform linear arrays (ULAs). The proposed method requires neither constructing the correlation matrix of the received data nor performing the singular value decomposition (SVD) of the correlation matrix. The problem is solved by dealing with three vectors composed of the first column, the first row and diagonal entries of the correlation matrix, which reduces the computational burden. Simultaneously, the proposed method utilizes the conjugate symmetry to enlarge the effective array aperture, which improves the estimation precision. The simulation results are presented to validate the effectiveness of the proposed algorithm. View full abstract»

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  • Efficient Iterative Estimation of the Parameters of a Damped Complex Exponential in Noise

    Page(s): 975 - 979
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (815 KB) |  | HTML iconHTML  

    The estimation of the frequency and decay factor of a single decaying exponential in noise is a problem of prime importance. A popular estimation scheme uses the computationally efficient implementation of the Discrete Fourier transform, the FFT, to obtain a coarse estimate which is then improved by a fine estimation stage. Such estimators, however, show a performance that degrades and departs from the Cramér-Rao Lower Bound (CRLB) as the number of samples increases. To overcome this problem, we propose an iterative, exponentially windowed algorithm. We derive the new estimator's theoretical performance and study its behavior under different decay rates of the window. We show that the estimator has excellent performance that tracks the CRLB with increasing number of samples if the window decay rate is appropriately set. View full abstract»

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  • Orthogonal Space Projection (OSP) Processing for Adaptive Interference Cancellation

    Page(s): 980 - 984
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (897 KB) |  | HTML iconHTML  

    An Orthogonal Space Projection (OSP) processing methodology is introduced that achieves interference cancellation within a single time compression interval, e.g. pulse interval for fast time or dwell interval for slow time. The OSP technique can operate in pre-compression or post-compression spaces where covariance matrix development and compression are performed simultaneously. OSP projects the signal and interference into a space that is matched to the signals of interest and into a space that is mismatched to the signals of interest similar to a blocking matrix in conventional adaptive processing methods. The output of the OSP process is a two-dimensional image space that combines signal compression and interference cancellation. A pre-compression OSP technique is applied to array processing for jammer suppression and a two-dimensional array example is used to assess SINR performance. View full abstract»

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  • A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization

    Page(s): 985 - 989
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1569 KB) |  | HTML iconHTML  

    We propose new optimization algorithms to minimize a sum of convex functions, which may be smooth or not and composed or not with linear operators. This generic formulation encompasses various forms of regularized inverse problems in imaging. The proposed algorithms proceed by splitting: the gradient or proximal operators of the functions are called individually, without inner loop or linear system to solve at each iteration. The algorithms are easy to implement and have proven convergence to an exact solution. The classical Douglas-Rachford and forward-backward splitting methods, as well as the recent and efficient algorithm of Chambolle-Pock, are recovered as particular cases. The application to inverse imaging problems regularized by the total variation is detailed. View full abstract»

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  • Condition Number-Constrained Matrix Approximation With Applications to Signal Estimation in Communication Systems

    Page(s): 990 - 993
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (586 KB) |  | HTML iconHTML  

    This letter introduces condition number-constrained approximation to matrices used for signal estimation and detection. Under a Frobenius norm criterion, the closed-form solution to the optimal approximation is derived, which can be found efficiently for arbitrary condition number constraints. The resulting approximation techniques are applied to the imperfectly estimated covariance and channel matrices used for estimating transmit signals in communication systems. With an appropriately chosen value of condition number, the robustness of the linear and decision-feedback estimators (DFE) against model mismatch can be significantly improved. View full abstract»

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  • Asymptotic Performance of Categorical Decision Making with Random Thresholds

    Page(s): 994 - 997
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (930 KB) |  | HTML iconHTML  

    In this letter, we investigate the asymptotic performance of categorical decision fusion in a human decision making framework. We assume that multiple human agents send categorized information to a moderator for final decision making. The local categorization is performed via a threshold based scheme where thresholds are assumed to be random variables. Considering the cases where the moderator has the knowledge of exact threshold values as well as when it has only probabilistic information of the individual thresholds, we analyze the asymptotic performance of likelihood ratio based decision fusion at the moderator in terms of the Chernoff information. Numerical results are presented for illustration. View full abstract»

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  • Multichannel Detection of an Unknown Rank-N Signal Using Uncalibrated Receivers

    Page(s): 998 - 1002
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1095 KB) |  | HTML iconHTML  

    This letter addresses the problem of detecting an unknown rank-N signal using multiple receivers that are uncalibrated in the sense that each applies an unknown scaling to the received signal and the (possibly unequal) receiver noise powers are unknown. This problem has been addressed for the case in which the signal can be modeled as a linear combination of N Gaussian random vectors. We consider the alternative approach of modeling the signal as a deterministic unknown. We derive an approximate generalized likelihood ratio test (GLRT) for low signal-to-noise ratios (SNRs). The resulting detector is invariant to relative scalings of the data, and is therefore constant false alarm rate (CFAR) with respect to the unknown noise powers. Numerical examples show this low-SNR GLRT performs well at all SNRs and can outperform other CFAR detectors when N = 1. However, CFAR detectors derived assuming unknown Gaussian signals appear to perform better for N > 1. View full abstract»

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  • PMFS: A Perceptual Modulated Feature Similarity Metric for Stereoscopic Image Quality Assessment

    Page(s): 1003 - 1006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (732 KB) |  | HTML iconHTML  

    Stereoscopic image quality assessment (SIQA) is an important and challenging issue in three dimensional applications. In this letter, a perceptual modulated feature similarity (PMFS) metric for SIQA is proposed by considering the monocular and binocular perception properties. Specifically, stereoscopic image is first classified into monocular occlusion and binocular rivalry regions. Then, feature similarities between the original and distorted stereoscopic images are defined and measured for the monocular occlusion and binocular rivalry regions as the local monocular and binocular quality maps, respectively. Monocular and binocular just noticeable difference visual saliency models are presented to construct a modulation function to derive monocular and binocular quality scores. Finally, those scores are integrated into an overall quality score by support vector regression. Extensive experiments performed on LIVE phase II and MICT asymmetric databases demonstrate that the proposed PMFS metric can achieve much higher consistency with the subjective quality scores than some state-of-the-art SIQA metrics. View full abstract»

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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu