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Signal Processing Letters, IEEE

Issue 11 • Date Nov. 2011

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Displaying Results 1 - 25 of 27
  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • IEEE Signal Processing Letters publication information

    Publication Year: 2011 , Page(s): C2
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  • Table of contents

    Publication Year: 2011 , Page(s): 617 - 618
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  • Recursive Bayesian Control of Multichannel Acoustic Echo Cancellation

    Publication Year: 2011 , Page(s): 619 - 622
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (930 KB) |  | HTML iconHTML  

    We present a novel recursive Bayesian method in the DFT-domain to address the multichannel acoustic echo cancellation problem. We model the echo paths between the loudspeakers and the near-end microphone as a multichannel random variable with a first-order Markov property. The incorporation of the near-end observation noise, in conjunction with the multichannel Markov model, leads to a multichannel state-space model. We derive a recursive Bayesian solution to the multichannel state-space model, which turns out to be well suited for input signals that are not only auto-correlated but also cross-correlated. We show that the resulting multichannel state-space frequency-domain adaptive filter (MCSSFDAF) can be efficiently implemented due to the submatrix-diagonality of the state-error covariance. The filter offers optimal tracking and robust adaptation in the presence of near-end noise and echo path variability. View full abstract»

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  • A Widely Linear Complex Unscented Kalman Filter

    Publication Year: 2011 , Page(s): 623 - 626
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1109 KB) |  | HTML iconHTML  

    Conventional complex valued signal processing algorithms assume rotation invariant (circular) signal distributions, and are thus suboptimal for real world processes which exhibit rotation dependent distributions (noncircular). In nonlinear sequential state space estimation, noncircularity can arise from the data, state transition model, and state and observation noises. We provide further insight by revisiting the augmented complex unscented Kalman filter (ACUKF) and illuminating its operation in such scenarios. The analysis establishes a relationship between the estimation error and the degree of second order noncircularity (improperness) in the system for the conventional complex unscented Kalman filter (CUKF), and is supported by simulations on both synthetic and real world proper and improper signals. View full abstract»

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  • Tagged Visual Cryptography

    Publication Year: 2011 , Page(s): 627 - 630
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1002 KB) |  | HTML iconHTML  

    This letter presents a method for implementing visual cryptography (VC) in which an additional tag is attached to each generated share. The proposed (t, n), 2 ≤ tn tagged visual cryptography (TVC) scheme works like a traditional VC scheme does, where the original image is encoded in n shares in such a way that the secret can be revealed by superimposing any t or more shares, but knowledge of less than t shares gets no secret information. A notable characteristic of TVC is that an extra tag can be revealed by folding up each share, which provides users with supplementary information such as augmented message or distinguishable patterns to identify the shares. The tagging property can easily be applied to any reported VC scheme to endow the generated shares with more capabilities. Construction methods and simulation results of the proposed (t , n) TVC based on conventional matrix-based VC and probabilistic-VC are illustrated in the letter. View full abstract»

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  • A Texture-Aware Salient Edge Model for Image Retargeting

    Publication Year: 2011 , Page(s): 631 - 634
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (902 KB) |  | HTML iconHTML  

    Image retargeting aims at adapting a given image to fit the size of arbitrary displays without severe visual distortions. To achieve this task successfully, it is essential to define a reliable image importance map (IIM) since it guides subsequent retargeting procedures. In this letter, we introduce a novel IIM for effective image retargeting. Specifically, we define our IIM by exploiting the higher order statistics (HOS) of the diffusion space for image retargeting. We call it texture-aware salient edge (TASE) map. Based on the proposed TASE map, we obtain visually acceptable retargeting results, even in the cluttered background and in the presence of noise as well. The proposed method has been extensively tested, and experimental results show that the proposed scheme is effective for image retargeting compared to other various state-of-the-art methods. View full abstract»

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  • Maximizing the Sum-Rate of Amplify-and-Forward Two-Way Relaying Networks

    Publication Year: 2011 , Page(s): 635 - 638
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    This letter addresses the problem of beamforming design for an amplify-and-forward (AF) based two-way relaying network (TWRN) which consists of two terminal nodes and several relay nodes. Considering a two-time-slot relaying scheme, we design the optimal beamforming coefficients to maximize the sum-rate of AF-based TWRN under total relay power constraint (TRPC). Although the optimization problem is neither convex nor concave, we show that the global optimal solution can be obtained by the branch-and-bound algorithm. To address the computational complexity concern, we also propose a low-complexity suboptimal solution which is obtained by optimizing a cost function over one real variable only. Simulation results show that the proposed optimal solution outperforms existing schemes significantly. Moreover, we show that the suboptimal solution only suffers small sum-rate losses compared to the optimal solution. View full abstract»

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  • Fast and Robust Circular Object Detection With Probabilistic Pairwise Voting

    Publication Year: 2011 , Page(s): 639 - 642
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (701 KB) |  | HTML iconHTML  

    Accurate and efficient detection of circular objects in images is a challenging computer vision problem. Existing circular object detection methods can be broadly classified into two categories: voting based and maximum likelihood estimation (MLE) based. The former is robust to noise, however its computational complexity and memory requirement are high. On the other hand, MLE based methods (e.g., robust least squares fitting) are more computationally efficient but sensitive to noise, and can not detect multiple circles. This letter proposes Probabilistic Pairwise Voting (PPV), a fast and robust algorithm for circular object detection based on an extension of Hough Transform. The main contributions are threefold. 1) We formulate the problem of circular object detection as finding the intersection of lines in the three dimensional parameter space (i.e., center and radius of the circle). 2) We propose a probabilistic pairwise voting scheme to robustly discover circular objects under occlusion, image noise and moderate shape deformations. 3) We use a mode-finding algorithm to efficiently find multiple circular objects. We demonstrate the benefits of our approach on two real-world problems: 1) detecting circular objects in natural images, and 2) localizing iris in face images. View full abstract»

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  • Tensor Locally Linear Discriminative Analysis

    Publication Year: 2011 , Page(s): 643 - 646
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1217 KB) |  | HTML iconHTML  

    This letter presents a Tensor Locally Linear Discriminative Analysis (TLLDA) method for image presentation. TLLDA is originated from the Local Fisher Discriminant Analysis (LFDA), but TLLDA offers some advantages over LFDA. 1) TLLDA can preserve the local discriminative information of image data as LFDA. 2) TLLDA represents images as matrices or 2-order tensors rather than vectors, so TLLDA keeps the spatial locality of pixels in the images. 3) TLLDA avoids the singularity that may be suffered by LFDA. 4) TLLDA is faster than LFDA. Simulations on two real databases verified the validity of TLLDA. Results show that TLLDA is highly competitive with some widely used techniques. View full abstract»

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  • Decentralized Coordinated Downlink Beamforming via Primal Decomposition

    Publication Year: 2011 , Page(s): 647 - 650
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (975 KB) |  | HTML iconHTML  

    This letter considers the design problem of coordinated downlink minimum power beamforming for a multiuser multi-cell network, where each multi-antenna base station (BS) serves multiple single antenna users. We propose a decentralized primal decomposition based algorithm where limited amount of information is exchanged between coupled BSs at each iteration. Algorithm converges to the globally optimal solution for a static scenario. Unlike most of the previous decentralized methods, a feasible set of beamformers is guaranteed at each iteration even when the exchanged backhaul information is outdated. Consequently, the proposed approach naturally lends itself to realistic time-correlated fading scenarios. View full abstract»

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  • Constant-Time Filtering Using Shiftable Kernels

    Publication Year: 2011 , Page(s): 651 - 654
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1214 KB) |  | HTML iconHTML  

    It was recently demonstrated in that the nonlinear bilateral filter can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in can be extended to few other linear and nonlinear filters . While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in , we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum , , and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (nonlinearshiftable) Gaussian kernel that is ubiquitously used in image filtering. View full abstract»

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  • A New Algorithm for Real Data Convolutions With j -Circulants

    Publication Year: 2011 , Page(s): 655 - 658
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (494 KB) |  | HTML iconHTML  

    A new algorithm for efficient linear convolution of real signals is presented. It is shown that the circulant required in traditional overlap-and-save (OLS) and overlap-and-add (OLA) methods can be substituted by a j-circulant, that is, a circulant matrix where the shifted elements are multiplied by the imaginary unit. Such j-circulant can be implemented easily and efficiently with half-length complex Fast Fourier Transforms. The latency remains the same as that of OLS and OLA. This method results in computational savings when compared to OLA and OLS, reducing the total arithmetic operations and particularly the execution time. View full abstract»

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  • An Adaptive Diffusion Augmented CLMS Algorithm for Distributed Filtering of Noncircular Complex Signals

    Publication Year: 2011 , Page(s): 659 - 662
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1167 KB) |  | HTML iconHTML  

    An adaptive diffusion augmented complex least mean square (D-ACLMS) algorithm for collaborative processing of the generality of complex signals over distributed networks is proposed. The algorithm enables the estimation of both second order circular (proper) and noncircular (improper) signals within a unified framework of augmented complex statistics. The analysis shows that the performance advantage of the widely linear D-ACLMS over the strictly linear D-CLMS increases with the degree of noncircularity while maintaining similar performance for proper data. Simulations on both synthetic benchmark and real world noncircular data support the approach. View full abstract»

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  • Dictionaries Construction Using Alternating Projection Method in Compressive Sensing

    Publication Year: 2011 , Page(s): 663 - 666
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (829 KB) |  | HTML iconHTML  

    This letter introduces a novel algorithm to construct sensing and measurement dictionaries in compressive sensing using alternating projection method. The cumulative and mutual cross coherence of the constructed dictionaries are lower than those of Gaussian random dictionary. The concept of General Restricted Isometry Constant (GRIC) is introduced. Low cumulative cross coherence puts bound on GRIC and small GRIC improves successful recovery rate of OMP algorithm. Experiments demonstrate that OMP algorithm performs better using dictionaries constructed by the proposed algorithm than Gaussian random dictionaries and those constructed by Schnass' algorithm. View full abstract»

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  • Probability Density of Weight Deviations Given Preceding Weight Deviations for Proportionate-Type LMS Adaptive Algorithms

    Publication Year: 2011 , Page(s): 667 - 670
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1438 KB) |  | HTML iconHTML  

    In this work, the conditional probability density function of the current weight deviations given the preceding weight deviations is generated for a wide array of proportionate type least mean square algorithms. The conditional probability density function is derived for colored input signals when noise is present as well as when noise is absent. Additionally, the marginal conditional probability density function for weight deviations is derived. Finally, potential applications of the derived conditional probability distributions are discussed and an example finding the steady-state probability distribution is presented. View full abstract»

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  • Timing Acquisition for Bandlimited Long-Code DS-CDMA in Doubly-Selective Fading Channels

    Publication Year: 2011 , Page(s): 671 - 674
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1407 KB) |  | HTML iconHTML  

    The code timing acquisition problem of bandlimited long-code based DS-CDMA systems operating in doubly-selective time-varying multipath channels is considered. We extend the unstructured schemes proposed by Buzzi to long-code spreading, which first estimates the Symbol Weighted Composite Channel Impulse Response (SW-CCIR) vectors and then extracts the timing informations. We also propose novel structured acquisition schemes, which directly estimate the multipath propagation delays by exploiting the a priori knowledge of the chip waveform. Both the structured and unstructured schemes are blind, since they require no other a priori information but the aperiodic spreading code of the desired user. Our numerical results demonstrate that the structured acquisition significantly outperforms both its unstructured counterpart and the classic correlator-based acquisition at a reasonable complexity increase. View full abstract»

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  • Colored Noise Based Multicondition Training Technique for Robust Speaker Identification

    Publication Year: 2011 , Page(s): 675 - 678
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (745 KB) |  | HTML iconHTML  

    This letter proposes a colored noise based multicondition training technique for robust speaker identification in unknown noisy environments. The colored noise samples generation is based on filtering a white Gaussian sequence that leads to a power spectral density (PSD) proportional to 1/fβ, where β ∈ [0, 2]. Gaussian mixture models (GMM) are applied to obtain the speaker models using the noisy speech signals with a single signal-to-noise ratio (SNR). The colored noise based multicondition training is evaluated for the speaker identification task considering the test utterances corrupted with real acoustic noises and different values of SNR. The results show that the proposed technique outperforms the white noise based multicondition and the clean-speech training approaches. View full abstract»

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  • Fingerprint Singular Point Detection Based on Multiple-Scale Orientation Entropy

    Publication Year: 2011 , Page(s): 679 - 682
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (974 KB) |  | HTML iconHTML  

    This letter develops a novel method for fingerprint singular point detection based on a new singularity representation of ridge-valley region called orientation entropy. The candidate singular point is obtained by the multiple-scale analysis of orientation entropy and some post processing steps are proposed to filter the spurious core and delta points. An iteration compensation scheme is proposed to search the precise location for core points against the offset further. Performance of the proposed method has been evaluated on the dataset of FVC2002 DB1. Experimental results show that the multiple-scale orientation entropy is correct and effective for singular detection and the location compensation scheme reduces the distance between the detection result and the truth singular point. View full abstract»

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  • Efficient Learning of Sample-Specific Discriminative Features for Scene Classification

    Publication Year: 2011 , Page(s): 683 - 686
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1085 KB) |  | HTML iconHTML  

    Learning the sample-specific discriminative features based on numerous local learning may not scale well to real world scene classification tasks and suffer from the risk of overfitting. Hence we cast it in SVM based localized multiple kernel learning framework, and design a new strategy to alternately optimize the standard SVM solver and the sample-specific kernel weights, by either a linear programming (for l1 -norm) or with closed-form solutions (for lp-norm). Experiments on both natural scene dataset and cluttered indoor scene dataset demonstrate the effectiveness and efficiency of our approach. View full abstract»

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  • Fast Radix-2 Algorithm for the Discrete Hartley Transform of Type II

    Publication Year: 2011 , Page(s): 687 - 689
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    We present a new efficient method for the computation of the discrete Hartley transform of type II and radix-2 length N=2n. This recursive method requires a reduced number of arithmetic operations compared with existing methods and can be easily implemented. A new efficient method for the direct computation of a length N type-II DHT from two adjacent DHT-II sequences of length N/2 is also presented. View full abstract»

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  • Visual Conspicuity Index: Spatial Dissimilarity, Distance, and Central Bias

    Publication Year: 2011 , Page(s): 690 - 693
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    We propose an image conspicuity index that combines three factors: spatial dissimilarity, spatial distance and central bias. The dissimilarity between image patches is evaluated in a reduced dimensional principal component space and is inversely weighted by the spatial separations between patches. An additional weighting mechanism is deployed that reflects the bias of human fixations towards the image center. The method is tested on three public image datasets and a video clip to evaluate its performance. The experimental results indicate highly competitive performance despite the simple definition of the proposed index. The conspicuity maps generated are more consistent with human fixations than prior state-of-the-art models when tested on color image datasets. This is demonstrated using both receiver operator characteristics (ROC) analysis and the Kullback-Leibler distance metric. The method should prove useful for such diverse image processing tasks as quality assessment, segmentation, search, or compression. The high performance and relative simplicity of the conspicuity index relative to other much more complex models suggests that it may find wide usage. View full abstract»

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  • Improved Block Truncation Coding Using Extreme Mean Value Scaling and Block-Based High Speed Direct Binary Search

    Publication Year: 2011 , Page(s): 694 - 697
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2114 KB) |  | HTML iconHTML  

    Block truncation coding (BTC) has been considered as an efficient compression technique for decades. However, the annoying blocking effect and false contour caused by low bit rate configuration are key problems. To solve these problems, many former halftoning-based BTCs are proposed. However, these schemes also induce another impulse noise issue while the previous issues still have room for improvement. To cope with this, the iteration-based halftoning is combined with BTC, namely Direct-Binary-Search BTC (DBSBTC) to solve the aforementioned problems. Moreover, the high-speed DBS halftoning method along with the block-based strategy yield even faster processing speed than some of the former halftoning-based BTC schemes. As documented in the experimental results, the proposed DBSBTC is superior to the former halftoning-base BTC schemes in terms of image quality, and which makes the former schemes as potential candidates for surveillance and computer vision applications. View full abstract»

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    Publication Year: 2011 , Page(s): 698
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  • IEEE Signal Processing Letters Information for authors

    Publication Year: 2011 , Page(s): 699 - 700
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    Freely Available from IEEE

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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Meet Our Editors

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