Discovering Thematic Objects in Image Collections and Videos

MAY 2012

Content GAzetteIeee SIGnAl ProCeSSInG SoCIetY

www.signalprocessingsociety.org [1] MAY 2012


APRIL 2012 VOLUME 60 NUMBER 4 ITPRED (ISSN 1053-587X)

REGULAR PAPERS

Statistical Signal ProcessingRobust Estimation of Noise Standard Deviation in Presence of Signals With Unknown Distributions and Occurrences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Pastor and F.-X. Socheleau 1545

Rooting-Based Harmonic Retrieval Using Multiple Shift-Invariances: The Complete and the Incomplete Sample Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Parvazi, M. Pesavento, and A. B. Gershman 1556

Robust Nonparametric Regression via Sparsity Control With Application to Load Curve Data Cleansing . . . . . .. . . . . . G. Mateos and G. B. Giannakis 1571

Adaptive Signal ProcessingAdaptive Data Fusion for Wireless Localization in Harsh Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Prieto, S. Mazuelas, A. Bahillo, P. Fernández, R. M. Lorenzo, and E. J. Abril 1585

Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals . . . . . . .. . . . . . . Q. Barthélemy, A. Larue, A. Mayoue, D. Mercier, and J. I. Mars 1597Robust Design of Adaptive Equalizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Rupp 1612

Digital and Multirate Signal ProcessingShift-Invariant and Sampling Spaces Associated With the Fractional Fourier Transform Domain . . . . . . . . . . . .. . . . . . . . . . . . A. Bhandari and A. I. Zayed 1627

Machine LearningDirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Rabaoui, N. Viandier, E. Duos, J. Marais, and P. Vanheeghe 1638

Hyperspectral Image Unmixing Using a Multiresolution Sticky HDP . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . R. Mittelman, N. Dobigeon, and A. O. Hero 1656Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis . . .. . . M. Anderson, T. Adal, and X.-L. Li 1672Kernel Sparse Representation-Based Classier . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li 1684

Sensor Array and Multichannel ProcessingRobust Secure Transmission in MISO Channels Based on Worst-Case Optimization . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . J. Huang and A. L. Swindlehurst 1696Broadband Underwater Localization of Multiple Sources Using Basis Pursuit De-Noising . . . . . . . . . . . .. . . . . . . . . . . . C. Liu, Y. V. Zakharov, and T. Chen 1708Adaptive Compressed Sensing Radar Oriented Toward Cognitive Detection in Dynamic Sparse Target Scene . . .. . . J. Zhang, D. Zhu, and G. Zhang 1718Optimization of the Receive Filter and Transmit Sequence for Active Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Stoica, H. He, and J. Li 1730The Multiple Model CPHD Tracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Georgescu and P. Willett 1741

Signal Processing for CommunicationsRobust Rate-Adaptive Wireless Communication Using ACK/NAK-Feedback . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . C. E. Koksal and P. Schniter 1752Opportunistic Distributed Space-Time Coding for Decode-and-Forward Cooperation Systems . . . . . . . . . . .. . . . . . . . . . . Y. Zou, Y.-D. Yao, and B. Zheng 1766Parameterized Cancellation of Partial-Band Partial-Block-Duration Interference for Underwater Acoustic OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z.-H. Wang, S. Zhou, J. Catipovic, and P. Willett 1782

Active Cooperation Between Primary Users and Cognitive Radio Users in Heterogeneous Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Su, J. D. Matyjas, and S. Batalama 1796

(Contents Continued on Back Cover)

www.signalprocessingsociety.org [2] MAY 2012 www.signalprocessingsociety.org [3] MAY 2012

(Contents Continued from Front Cover)

Detecting and Counteracting Statistical Attacks in Cooperative Spectrum Sensing . . . . . . . . . . .. . . . . . . . . . . F. Penna, Y. Sun, L. Dolecek, and D. Cabric 1806On Multiple Antenna Spectrum Sensing Under Noise Variance Uncertainty and Flat Fading . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . J. K. Tugnait 1823Sensing and Probing Cardinalities for Active Cognitive Radios . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . T. V. Nguyen, H. Shin, T. Q. S. Quek, and M. Z. Win 1833

MIMO Communications & Signal ProcessingSpace-Time Processing for X Channels Using Precoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Li and H. Jafarkhani 1849Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems With Arbitrary Power Constraint: Generalized Duality Approach . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. E. Bogale and L. Vandendorpe 1862

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. E. Bogale and L. Vandendorpe 1876

Minimal Transmit Power in Parallel Vector Broadcast Channels With Linear Precoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Hellings, M. Joham, M. Riemensberger, and W. Utschick 1890

Monotonic Optimization Framework for Coordinated Beamforming in Multicell Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Utschick and J. Brehmer 1899Beam Tracking for Interference Alignment in Slowly FadingMIMO Interference Channels: A Perturbations Approach Under a Linear Framework . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Yu, Y. Sung, H. Kim, and Y. H. Lee 1910

Signal Processing for Sensor NetworksFilter-and-Forward Distributed Beamforming for Two-Way Relay Networks With Frequency Selective Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Chen, S. Shahbazpanahi, and A. B. Gershman 1927

Distributed Basis Pursuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. F. C. Mota, J. M. F. Xavier, P. M. Q. Aguiar, and M. Püschel 1942

Signal Processing for Wireless NetworksConvex Approximation Algorithms for Back-Pressure Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Matskani, N. D. Sidiropoulos, and L. Tassiulas 1957Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Sun, X. Zhong, T.-H. Chang, S. Zhou, J. Wang, and C.-Y. Chi 1971

Data Demand Dynamics in Wireless Communications Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ren and M. van der Schaar 1986Designing Router Scheduling Policies: A Privacy Perspective . . . . . . . . . . . .. . . . . . . . . . . . S. Kadloor, X. Gong, N. Kiyavash, and P. Venkitasubramaniam 2001

Biomedical Signal ProcessingMaximum-Parsimony Haplotype Inference Based on Sparse Representations of Genotypes . . . . . . . . . . . . .. . . . . . . . . . . . . G. H. Jajamovich and X. Wang 2013

Implementation of Signal Processing SystemsThroughput-Distortion Computation of Generic Matrix Multiplication: Toward a Computation Channel for Digital Signal Processing Systems . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Anastasia and Y. Andreopoulos 2024

CORRESPONDENCE

Statistical Signal ProcessingScheduling Two Gauss–Markov Systems: An Optimal Solution for Remote State Estimation Under Bandwidth Constraint . .. . . L. Shi and H. Zhang 2038Single-Transmission Distributed Detection via Order Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . P. Braca, S. Marano, and V. Matta 2042

Adaptive Signal ProcessingA Class of Adaptive Algorithms Based on Entropy Estimation Achieving CRLB for Linear Non-Gaussian Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Li, X.-L. Li, M. Anderson, and T. Adal 2049

A New Variable Step-Size NLMS Algorithm and Its Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . H.-C. Huang and J. Lee 2055

Digital and Multirate Signal ProcessingThe Design of Hybrid Symmetric-FIR/Analog Pulse-Shaping Filters . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . C.-Y. Yao and A. N. Willson 2060

Sensor Array and Multichannel ProcessingEfcient Application of MUSIC Algorithm Under the Coexistence of Far-Field and Near-Field Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. He, M. N. S. Swamy, and M. O. Ahmad 2066

Effects of Model Mismatch in MIMO Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Friedlander 2071On MIMO Radar Subarrayed Transmit Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Wilcox and M. Sellathurai 2076

Signal Processing for CommunicationsOptimal HDA Schemes for Transmission of a Gaussian Source Over a Gaussian Channel With Bandwidth Compression in the Presence of anInterference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Varasteh and H. Behroozi 2081

MIMO Communications & Signal ProcessingJoint Source-Channel Coding for the MIMO Broadcast Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Persson, J. Kron, M. Skoglund, and E. G. Larsson 2085

Signal Processing for Wireless NetworksWeighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. K. Joshi, P. C. Weeraddana, M. Codreanu, and M. Latva-aho 2090

EDICS—Editors’ Information Classication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2096Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2097


MARCH 2012 VOLUME 20 NUMBER 3 ITASD8 (ISSN 1558-7916)

REGULAR PAPERS

Loudspeaker and Microphone Array Signal Processing

Optimal Higher Order Ambisonics Encoding With Predefined Constraints . . . . . . . . . .. . . . . . . . . . H. Sun, S. Yan, and U. P. Svensson 742A Speech Distortion and Interference Rejection Constraint Beamformer . . . . . . . . . . E. A. P. Habets, J. Benesty, and P. A. Naylor 854A Perspective on Frequency-Domain Beamformers in Room Acoustics . . . . . . . . . . . . . . . . . . . . . . . . . . E. A. P. Habets and J. Benesty 954

Active Noise Control

Performance Analysis and Design of FxLMS Algorithm in Broadband ANC System With Online Secondary-Path Modeling . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. C. Chan and Y. Chu 982

Auditory Modeling and Hearing Aids

Adaptive Gain Processing With Offending Frequency Suppression for Digital Hearing Aids . . .. . . A. Pandey and V. J. Mathews 1043

Source Separation and Signal Enhancement

A Forced Spectral Diversity Algorithm for Speech Dereverberation in the Presence of Near-Common Zeros . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Lin, A. W. H. Khong, and P. A. Naylor 888

A Wiener Filter Approach to Microphone Leakage Reduction in Close-Microphone Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. K. Kokkinis, J. D. Reiss, and J. Mourjopoulos 767

Audio Analysis and Synthesis

Automatic Transcription of Guitar Chords and Fingering From Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. M. Barbancho, A. Klapuri, L. J. Tardón, and I. Barbancho 915

Audio Inpainting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Adler, V. Emiya, M. G. Jafari, M. Elad, R. Gribonval, and M. D. Plumbley 922Performance Control Driven Violin Timbre Model Based on Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Pérez Carrillo, J. Bonada, E. Maestre, E. Guaus, and M. Blaauw 1007




Content-Based Audio Processing

A Nonparametric Bayesian Multipitch Analyzer Based on Infinite Latent Harmonic Allocation . . . . . . . . K. Yoshii and M. Goto 717Automatic Transcription of Bell Chiming Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Marolt 844

Speech Analysis

A Normalized Beamforming Algorithm for Broadband Speech Using a Continuous Interleaved Sampling Strategy . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Chen and Q. Gong 868

SAFE: A Statistical Approach to F0 Estimation Under Clean and Noisy Conditions . . . . . . . . . . . . . . . . . . . . . . W. Chu and A. Alwan 933The Deterministic Plus Stochastic Model of the Residual Signal and Its Applications . . . . . . . . .. . . . . . . . T. Drugman and T. Dutoit 968Detection of Glottal Closure Instants From Speech Signals: A Quantitative Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Drugman, M. Thomas, J. Gudnason, P. Naylor, and T. Dutoit 994

Speech Synthesis and Generation

Product of Experts for Statistical Parametric Speech Synthesis . . . . . . . . . . . . . . H. Zen, M. J. F. Gales, Y. Nankaku, and K. Tokuda 794

Speech Coding

Fast Algorithms for Low-Delay SBR Filterbanks in MPEG-4 AAC-ELD .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. K. Chivukula, Y. A. Reznik, V. Devarajan, and M. Jayendra-Lakshman 1022

Quality Preserving Compression of a Concatenative Text-To-Speech Acoustic Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Shoham, D. Malah, and S. Shechtman 1056

Acoustic Modeling for Automatic Speech Recognition

Automatic Speech Recognition Based on Non-Uniform Error Criteria . . . . . . . . . . . . . . . . . . . . . . . . . Q. Fu, Y. Zhao, and B.-H. Juang 780

Robust Speech Recognition

Voice Conversion Using Dynamic Kernel Partial Least Squares Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Helander, H. Silén, T. Virtanen, and M. Gabbouj 806

Combining Speech Fragment Decoding and Adaptive Noise Floor Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Ma, J. Barker, H. Christensen, and P. Green 818

Modulation Spectrum Equalization for Improved Robust Speech Recognition . . . . . . . . . . . . . .. . . . . . . . . . . . . . L.-C. Sun and L.-S. Lee 828Learning-Based Auditory Encoding for Robust Speech Recognition . . . . . . . . . . . . . . . . . . . . . . Y.-H. B. Chiu, B. Raj, and R. M. Stern 900

Multilingual Recognition and Identification

Experiments on Cross-Language Attribute Detection and Phone Recognition With Minimal Target-Specific Training Data . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. M. Siniscalchi, D.-C. Lyu, T. Svendsen, and C.-H. Lee 875

Speaker Characterization and Recognition

Source-Normalized LDA for Robust Speaker Recognition Using i-Vectors From Multiple Speech Sources . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. McLaren and D. van Leeuwen 755

Psychoacoustic Model Compensation for Robust Speaker Verification in Environmental Noise . . . . A. Panda and T. Srikanthan 945Variational Bayesian Joint Factor Analysis Models for Speaker Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Zhao and Y. Dong 1032

Speech Data Mining and Document Retrieval

Performance Analysis and Improvement of Turkish Broadcast News Retrieval . . . . . . . . . . . .. . . . . . . . . . . . S. Parlak and M. Saraçlar 731

CORRESPONDENCE

Speech Coding

Nonlinear Long-Term Prediction of Speech Based on Truncated Volterra Series . . . . .. . . . V. Despotovic, N. Goertz, and Z. Peric 1069

Echo Cancellation

A Spectral Slit Approach to Doubletalk Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Y. Low, S. Venkatesh, and S. Nordholm 1074

EDICS—Editor’s Information and Classification Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1081Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083


APRIL 2012 VOLUME 21 NUMBER 4 IIPRE4 (ISSN 1057-7149)

PAPERS

Image & Video Sensing and AcquisitionBits From Photons: Oversampled Image Acquisition Using Binary Poisson Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Yang, Y. M. Lu, L. Sbaiz, and M. Vetterli 1421Statistical-Model Based Methods

Bayesian Inference of Models and Hyperparameters for Robust Optical-Flow Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Héas, C. Herzet, and E. Mémin 1437Image & Video Representation

Wavelet Modeling Using Finite Mixtures of Generalized Gaussian Distributions: Application to Texture Discriminationand Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Allili 1452

Rotation-Invariant Image and Video Description With Local Binary Pattern Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Zhao, T. Ahonen, J. Matas, and M. Pietikäinen 1465

Blind Adaptive Sampling of Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Devir and M. Lindenbaum 1478Perception and Quality Models for Images & Video

On the Mathematical Properties of the Structural Similarity Index . . . . . . . . . . . . . . . . . . D. Brunet, E. R. Vrscay, and Z. Wang 1488Image Quality Assessment Based on Gradient Similarity . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . A. Liu, W. Lin, and M. Narwaria 1500Removing Label Ambiguity in Learning-Based Visual Saliency Estimation . . . . . . . . . . .. . . . . . . . . . . J. Li, D. Xu, and W. Gao 1513Quaternion Structural Similarity: A New Quality Index for Color Images . . . . . . . . . . . . . . . . A. Kolaman and O. Yadid-Pecht 1526Linear and Nonlinear Filtering of Images & Video

Iterative Truncated Arithmetic Mean Filter and Its Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Jiang 1537Edge-Preserving Image Regularization Based on Morphological Wavelets and Dyadic Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. J. Xiang and P. J. Ramadge 1548

(Contents Continued on Page 1417)



Partial Differential Equation Based Processing of Images & VideoAn Edge-Adapting Laplacian Kernel For Nonlinear Diffusion Filters . . .. . . M. R. Hajiaboli, M. O. Ahmad, and C. Wang 1561Locally Oriented Optical Flow Computation . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . Y. Niu, A. Dick, and M. Brooks 1573On the Construction of Topology-Preserving Deformation Fields . . . . . . . .. . . . . . . . C. Le Guyader, D. Apprato, and C. Gout 1587Multiple-Region Segmentation Without Supervision by Adaptive Global Maximum Clustering . . . . S. Kim and M. Kang 1600Multiresolution Processing of Images & Video

Fast Wavelet-Based Image Characterization for Highly Adaptive Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, and C. Roux 1613Restoration and Enhancement

Robust Image Deblurring With an Inaccurate Blur Kernel . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . H. Ji and K. Wang 1624Patch-Based Near-Optimal Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . P. Chatterjee and P. Milanfar 1635Spatially Adapted Total Variation Model to Remove Multiplicative Noise . . . . . . . . . . . . . . . . . . . D.-Q. Chen and L.-Z. Cheng 1650A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means . . . . . . . . . . . . . B. Xiong and Z. Yin 1663An Iterative -Based Image Restoration Algorithm With an Adaptive Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. B. Montefusco and D. Lazzaro 1676

Robust Multichannel Blind Deconvolution via Fast Alternating Minimization . . . . . . . .. . . . . . . . . F. Šroubek and P. Milanfar 1687Alternating Minimization Algorithm for Speckle Reduction With a Shifting Technique . . . . . . . .. . . . . . . . H. Woo and S. Yun 1701BM3D Frames and Variational Image Deblurring . . . . . . . . . . . . . . . . . . . . . . . . . . A. Danielyan, V. Katkovnik, and K. Egiazarian 1715Camera-Pose Estimation via Projective Newton Optimization on the Manifold . . . . . . . . . . . . . . . . M. Sarkis and K. Diepold 1729Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L.-W. Kang, C.-W. Lin, and Y.-H. Fu 1742

Underwater Image Enhancement by Wavelength Compensation and Dehazing . . . . . . . . . . . . . . J. Y. Chiang and Y.-C. Chen 1756Parameter Selection for Total-Variation-Based Image Restoration Using Discrepancy Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y.-W. Wen and R. H. Chan 1770Interpolation, Super-Resolution, and Mosaicing

Super-Resolution Without Dense Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Su, Y. Wu, and J. Zhou 1782Commutability of Blur and Afne Warping in Super-Resolution With Application to Joint Estimation of Triple-CoupledVariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Zhang, J. Jiang, and S. Peng 1796Formation and Reconstruction

A Variational Method for Multiple-Image Blending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Wang and M. K. Ng 1809Biomedical and Biological Image Processing

A General Fast Registration Framework by Learning Deformation–Appearance Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Kim, G. Wu, P.-T. Yap, and D. Shen 1823

Sparse Poisson Noisy Image Deblurring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Carlavan and L. Blanc-Féraud 1834Principal Curves for Lumen Center Extraction and Flow Channel Width Estimation in 3-D Arterial Networks: Theory,Algorithm, and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. C. K. Wong, R. W. K. So, and A. C. S. Chung 1847

Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Temerinac-Ott, O. Ronneberger, P. Ochs, W. Driever, T. Brox, and H. Burkhardt 1863Lossy Coding of Images & Video

Jointly Optimized Spatial Prediction and Block Transform for Video and Image Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Han, A. Saxena, V. Melkote, and K. Rose 1874

Image Prediction Based on Neighbor-Embedding Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Türkan and C. Guillemot 1885Edge-Based Perceptual Image Coding . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Niu, X. Wu, G. Shi, and X. Wang 1899Adaptive Quantization-Parameter Clip Scheme for Smooth Quality in H.264/AVC .. . . . . S. Hu, H. Wang, and S. Kwong 1911Scanning Order Strategies for Bitplane Image Coding . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . F. Aulí-Llinàs and M. W. Marcellin 1920Side-Information-Dependent Correlation Channel Estimation in Hash-Based Distributed Video Coding . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Deligiannis, J. Barbarien, M. Jacobs, A. Munteanu, A. Skodras, and P. Schelkens 1934

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Rahmoune, P. Vandergheynst, and P. Frossard 1950Image & Video Processing for Watermarking and Security

Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Li, Z. Lu, C. Zhu, and X. Niu 1963Image Scanning and Capture

Design and Optimization of Color Lookup Tables on a Simplex Topology . . . . . . . . . . . . . . . . . V. Monga, R. Bala, and X. Mo 1981



(Contents Continued from Page 1417)

Color and Multispectral ImagingColor Constancy by Category Correlation . . . . . . . . . . . . . . . . . . . . . . . . J. Vazquez-Corral, M. Vanrell, R. Baldrich, and F. Tous 1997Spectral–Spatial Classication of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson 2008Printing and Halftoning

Alleviating Dirty-Window Effect in Medium Frame-Rate Binary Video Halftones . . . . . .. . . . . . H. Rehman and B. L. Evans 2022Tomographic Imaging

Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Chen, J. K. Udupa, U. Bagci, Y. Zhuge, and J. Yao 2035Magnetic Resonance Imaging

A Markov Random Field Approach for Topology-Preserving Registration: Application to Object-Based TomographicImage Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . L. Cordero-Grande, G. Vegas-Sánchez-Ferrero, P. Casaseca-de-la-Higuera, and C. Alberola-López 2047Radar Imaging, Remote Sensing, and Geophysical Imaging

Single Frequency Inverse Obstacle Scattering: A Sparsity Constrained Linear Sampling Method Approach . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. F. Alqadah, M. Ferrara, H. Fan, and J. T. Parker 2062

A Sparsity-Driven Approach for Joint SAR Imaging and Phase Error Correction . . . . . . .. . . . . . . N. Ö. Önhon and M. Çetin 2075Region, Boundary, and Shape Analysis

Implicit Polynomial Representation Through a Fast Fitting Error Estimation . . . . . . . . . . . . . . . M. Rouhani and A. D. Sappa 2089Correlation-Coefcient-Based Fast Template Matching Through Partial Elimination . . . . . . . . . . A. Mahmood and S. Khan 2099JUDOCA: JUnction Detection Operator Based on Circumferential Anchors . . . . . . . . . . . . . . . . . . . . R. Elias and R. Laganière 2109Interactive Image Segmentation Using Dirichlet Process Multiple-View Learning . . . . . . . . L. Ding, A. Yilmaz, and R. Yan 2119Image & Video Mid Level Analysis

Scale- and Rotation-Invariant Local Binary Pattern Using Scale-Adaptive Texton and Subuniform-Based Circular Shift . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Li, G. Liu, Y. Yang, and J. You 2130Image & Video Interpretation and Understanding

Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Gong, Z. Zhou, and J. Ma 2141

Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H.-Y. Cheng, C.-C. Weng, and Y.-Y. Chen 2152

Counting People With Low-Level Features and Bayesian Regression . . . . . . . . . . . . . . . . . . . . . A. B. Chan and N. Vasconcelos 2160Fast 2-D Distance Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Krinidis 2178A Discriminative Model of Motion and Cross Ratio for View-Invariant Action Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Huang, Y. Zhang, and T. Tan 2187

Scale-Aware Saliency for Application to Frame Rate Upconversion . . . . . . . . . . . . . . . . . . . . . . . N. Jacobson and T. Q. Nguyen 2198Discovering Thematic Objects in Image Collections and Videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Yuan, G. Zhao, Y. Fu, Z. Li, A. K. Katsaggelos, and Y. Wu 2207Image & Video Biometric Analysis

Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Gottschlich 2220

Human Identication Using Finger Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Kumar and Y. Zhou 2228Face Identication Using Large Feature Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. R. Schwartz, H. Guo, J. Choi, and L. S. Davis 2245Gait Recognition With Shifted Energy Image and Structural Feature Extraction . . . . . . . . . . X. Huang and N. V. Boulgouris 2256Image & Video Storage and Retrieval

Camera Constraint-Free View-Based 3-D Object Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Gao, J. Tang, R. Hong, S. Yan, Q. Dai, N. Zhang, and T.-S. Chua 2269

Task-Dependent Visual-Codebook Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Ji, H. Yao, W. Liu, X. Sun, and Q. Tian 2282Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval . . . . . L. Zhang, L. Wang, and W. Lin 2294




CORRESPONDENCE

Perception and Quality Models for Images & VideoMonotonic Regression: A New Way for Correlating Subjective and Objective Ratings in Image Quality Research . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Han, Y. Cai, Y. Cao, and X. Xu 2309Multiresolution Processing of Images & Video

Boundary Operation of 2-D Nonseparable Linear-Phase Paraunitary Filter Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Muramatsu, T. Kobayashi, M. Hiki, and H. Kikuchi 2314Restoration and Enhancement

Gradient-Directed Multiexposure Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Zhang and W.-K. Cham 2318Polyview Fusion: A Strategy to Enhance Video-Denoising Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Zeng and Z. Wang 2324Removing Boundary Artifacts for Real-Time Iterated Shrinkage Deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Šorel 2329Microscopic Imaging

Methodology for Reconstructing Early Zebrash Development From In VivoMultiphoton Microscopy . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. A. Luengo-Oroz, J. L. Rubio-Guivernau, E. Faure, T. Savy, L. Duloquin,N. Olivier, D. Pastor, M. Ledesma-Carbayo, D. Débarre, P. Bourgine, E. Beaurepaire, N. Peyriéras, and A. Santos 2335Image & Video Interpretation and Understanding

Particle Filter With a Mode Tracker for Visual Tracking Across Illumination Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Das, A. Kale, and N. Vaswani 2340Image & Video Biometric Analysis

Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. H. Lee, J. Y. Choi, Y. M. Ro, and K. N. Plataniotis 2347Image & Video Storage and Retrieval

Semantic-Gap-Oriented Active Learning for Multilabel Image Annotation . . . . J. Tang, Z.-J. Zha, D. Tao, and T.-S. Chua 2354

COMMENTS AND CORRECTIONS

Corrections to “Real-Time Artifact Free Image Upscaling” . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . A. Giachetti and N. Asuni 2361

EDICS—Editor’s Information Classication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2362Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2363


APRIL 2012 VOLUME 7 NUMBER 2 ITIFA6 (ISSN 1556-6013)

EDITORIAL

From the New Editor-in-Chief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.-C. J. Kuo 357

PAPERS

System ModelsSecure Communication Over Parallel Relay Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. H. Awan, A. Zaidi, and L. Vandendorpe 359

Watermarking and Data Hiding/EmbeddingA Weak Security Notion for Visual Secret Sharing Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Iwamoto 372A Dual-Channel Time-Spread Echo Method for Audio Watermarking . . . . . . Y. Xiang, I. Natgunanathan, D. Peng, W. Zhou, and S. Yu 383Enhancing Source Camera Identication Performance With a Camera Reference Phase Sensor Pattern Noise . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Kang, Y. Li, Z. Qu, and J. Huang 393

Interference Removal Operation for Spread Spectrum Fingerprinting Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Kuribayashi 403Nonparametric Steganalysis of QIM Steganography Using Approximate Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Malik, K. P. Subbalakshmi, and R. Chandramouli 418

Ensemble Classiers for Steganalysis of Digital Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Kodovský, J. Fridrich, and V. Holub 432From Blind to Quantitative Steganalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Pevný, J. Fridrich, and A. D. Ker 445Data Hiding in MPEG Video Files Using Multivariate Regression and Flexible Macroblock Ordering . . . . . . . . . . . . . . . . . . . T. Shanableh 455

Cryptographic and Related TechniquesA Practical Framework for -Out-of- Oblivious Transfer With Security Against Covert Adversaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Zeng, C. Tartary, P. Xu, J. Jing, and X. Tang 465

A Unied Framework for Key Agreement Over Wireless Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Lai, Y. Liang, and H. V. Poor 480On Statistical Tests for Randomness Included in the NIST SP800-22 Test Suite and Based on the Binomial Distribution . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Pareschi, R. Rovatti, and G. Setti 491

BiometricsHeterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose . . . . . . . . . . . . X. Zhang and Y. Gao 506




Index Codes for Multibiometric Pattern Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Gyaourova and A. Ross 518Activity-Based Person Identication Using Fuzzy Representation and Discriminant Learning . . . .. . . . A. Iosidis, A. Tefas, and I. Pitas 530The Effect of Time on Gait Recognition Performance . . . . . . . . . . .. . . . . . . . . . . D. S. Matovski, M. S. Nixon, S. Mahmoodi, and J. N. Carter 543Cross-Pollination of Normalization Techniques From Speaker to Face Authentication Using Gaussian Mixture Models . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Wallace, M. McLaren, C. McCool, and S. Marcel 553

3-D Generic Elastic Models for Fast and Texture Preserving 2-D Novel Pose Synthesis . . . . . . . . . . . . . . . . . . . . . . . . J. Heo and M. Savvides 5633-D Face Recognition With Local Shape Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. İnan and U. Halici 577Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. P. Hollingsworth, S. S. Darnell, P. E. Miller, D. L. Woodard, K. W. Bowyer, and P. J. Flynn 588

Local Ordinal Contrast Pattern Histograms for Spatiotemporal, Lip-Based Speaker Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. H. Chan, B. Goswami, J. Kittler, and W. Christmas 602

Binary Discriminant Analysis for Generating Binary Face Template . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . Y. C. Feng and P. C. Yuen 613

Forensic AnalysisRecognition of Brand and Models of Cell-Phones From Recorded Speech Signals . . . . C. Hanilçi, F. Ertaş, T. Ertaş, and Ö. Eskidere 625A System for Formal Digital Forensic Investigation Aware of Anti-Forensic Attacks . . . . . . . . . . . . . . . . . . . . . . . . S. Rekhis and N. Boudriga 635

Security and Privacy AnalysisoPass: A User Authentication Protocol Resistant to Password Stealing and Password Reuse Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H.-M. Sun, Y.-H. Chen, and Y.-H. Lin 651

Mitigating Routing Misbehavior in Disruption Tolerant Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Q. Li and G. Cao 664A Large-Scale Empirical Study of Concker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Shin, G. Gu, N. Reddy, and C. P. Lee 676Enhancing the Trust of Internet Routing With Lightweight Route Attestation . . . . Q. Li, M. Xu, J. Wu, X. Zhang, P. P. C. Lee, and K. Xu 691Secrecy Outage in MISO Systems With Partial Channel Information . . . . . . . . . . . . . . . . S. Gerbracht, C. Scheunert, and E. A. Jorswieck 704An Efcient Gradient Descent Approach to Secure Localization in Resource Constrained Wireless Sensor Networks . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Garg, A. L. Varna, and M. Wu 717

A New Bound on the Performance of the Bandwidth Puzzle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Zhang 731

System Design and ImplementationHASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Wan, J. Liu, and R. H. Deng 743

Fighting Mallory the Insider: Strong Write-Once Read-Many Storage Assurances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Sion and Y. Chen 755

ApplicationsGate Characterization Using Singular Value Decomposition: Foundations and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Wei, A. Nahapetian, M. Nelson, F. Koushanfar, and M. Potkonjak 765

Image Phylogeny by Minimal Spanning Trees . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . Z. Dias, A. Rocha, and S. Goldenstein 774RIHT: A Novel Hybrid IP Traceback Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M.-H. Yang and M.-C. Yang 789Toward Covert Iris Biometric Recognition: Experimental Results From the NICE Contests . . . . .. . . . . H. Proença and L. A. Alexandre 798Forensic Detection of Fraudulent Alteration in Ball-Point Pen Strokes . . . . . . .. . . . . . . R. Kumar, N. R. Pal, B. Chanda, and J. D. Sharma 809

CORRESPONDENCE

Watermarking and Data Hiding/EmbeddingControllable Secure Watermarking Technique for Tradeoff Between Robustness and Security . . . . . . . . . . . . . . . . . . . . J. Cao and J. Huang 821Separable Reversible Data Hiding in Encrypted Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Zhang 826

BiometricsA Framework for Analyzing Template Security and Privacy in Biometric Authentication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Simoens, J. Bringer, H. Chabanne, and S. Seys 833

Forensic AnalysisDetection of Nonaligned Double JPEG Compression Based on Integer Periodicity Maps . . . . . . . . . . . .. . . . . . . . . . . . T. Bianchi and A. Piva 842

EDICS—Editor’s Information Classication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 850

ANNOUNCEMENTS

Call for Papers—IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY Special Issue on Privacy and Trust Managementin Cloud and Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 852


APRIL 2012 VOLUME 14 NUMBER 2 ITMUF8 (ISSN 1520-9210)

PAPERS

Compression and Coding

An Advanced Hierarchical Motion Estimation Scheme With Lossless Frame Recompression and Early-Level Terminationfor Beyond High-Denition Video Coding . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . X. Bao, D. Zhou, P. Liu, and S. Goto 237

Low-Decoding-Latency Buffer Compression for Graphics Processing Units . . . . . . . S.-Y. Chien, K.-H. Lok, and Y.-C. Lu 250Efcient and Rate-Distortion Optimal Wavelet Packet Basis Selection in JPEG2000 . . . . . . . . . . . . . . . . . . T. Stütz and A. Uhl 264Robust Image Coding Based Upon Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . C. Deng, W. Lin, B. Lee, and C. T. Lau 278

Algorithms and Algorithmic Transformations

Efcient Genre-Specic Semantic Video Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Wu and M. Worring 291

Low-Power Digital and Analog Circuits for Multimedia

Reducing DRAM Image Data Access Energy Consumption in Video Processing . . . . . . . . . . . . . . . . . . . . . . Y. Li and T. Zhang 303

Perceptual Quality and Human Factors

Bridging the Semantic Gap via Functional Brain Imaging . . . . . . . . . . . . . . . X. Hu, K. Li, J. Han, X. Hua, L. Guo, and T. Liu 314Assessment of Stereoscopic Crosstalk Perception . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . L. Xing, J. You, T. Ebrahimi, and A. Perkis 326

Content Analysis and Synthesis

Automatic Role Recognition in Multiparty Conversations: An Approach Based on Turn Organization, Prosody, andConditional Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Salamin and A. Vinciarelli 338




Indexing, Searching, Retrieving, Query, and Archiving Databases

Path Modeling and Retrieval in Distributed Video Surveillance Databases . . . . L. Lo Presti, S. Sclaroff, and M. L. Cascia 346Weakly Supervised Graph Propagation Towards Collective Image Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Liu, S. Yan, T. Zhang, C. Xu, J. Liu, and H. Lu 361Investigating the Effects of Multiple Factors Towards More Accurate 3-D Object Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Daras, A. Axenopoulos, and G. Litos 374

Audio/Image/Video Segmentation for Interactive Services

Probabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation . . . . . . . . T. Wang and J. Collomosse 389

Error Resilience

Analytical Modeling for Delay-Sensitive Video Over WLAN .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . H. Bobarshad, M. van der Schaar, A. H. Aghvami, R. S. Dilmaghani, and M. R. Shikh-Bahaei 401

Multimedia Streaming

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Schier and M. Welzl 415

Wireless Multimedia Communication

QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Khan, L. Sun, and E. Ifeachor 431

Joint Source-Channel Coding and Optimization for Layered Video Broadcasting to Heterogeneous Devices . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Ji, Z. Li, and Y. Chen 443

WWW, Hypermedia and Internet, Internet II

Web Video Geolocation by Geotagged Social Resources . . . . Y.-C. Song, Y.-D. Zhang, J. Cao, T. Xia, W. Liu, and J.-T. Li 456Hierarchical Co-Clustering: A New Way to Organize the Music Data . . . . . . . . . . . . . . J. Li, B. Shao, T. Li, and M. Ogihara 471

CORRESPONDENCE

Content Analysis and Synthesis

Robustly Extracting Captions in Videos Based on Stroke-Like Edges and Spatio-Temporal Analysis . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Liu and W. Wang 482

COMMENTS AND CORRECTIONS

Correction to “Bayesian Visual Reranking” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Tian, L. Yang, J. Wang, X. Wu, and X.-S. Hua 490

EDICS—Editors Classication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

ANNOUNCEMENTS

Call for Papers—IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Special Issue on Privacy and TrustManagement in Cloud and Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494

Call for Papers—IEEE TRANSACTIONS ON MULTIMEDIA, Special Issue on Mobile Media Retrieval. . . . . . . . . . . . . . . . . . . . . . . 495


APRIL 2012 VOLUME 6 NUMBER 2 IJSTGY (ISSN 1932-4553)

ISSUE ON GAME THEORY IN SIGNAL PROCESSING

EDITORIAL

Introduction to the Issue on Game Theory in Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. A. Jorswieck, E. G. Larsson, M. Luise, H. V. Poor, and A. Leshem 73

PAPERS

A Pre-Bayesian Game for CDMA Power Control During Network Association . . . . . . . . . . . . . . . . . . . . G. Bacci and M. Luise 76Potential Games for Energy-Efficient Power Control and Subcarrier Allocation in Uplink Multicell OFDMA Systems . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Buzzi, G. Colavolpe, D. Saturnino, and A. Zappone 89Quality-Of-Service Provisioning in Decentralized Networks: A Satisfaction Equilibrium Approach . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. M. Perlaza, H. Tembine, S. Lasaulce, and M. Debbah 104Congestion, Information, and Secret Information in Flow Networks . . . . K. T. Phan, M. van der Schaar, and W. R. Zame 117Applying Bargaining Solutions to Resource Allocation in Multiuser MIMO-OFDMA Broadcast Systems . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Chen and A. L. Swindlehurst 127Dynamic Pricing and Queue Stability in Wireless Random Access Games . . . . . . . . Y. Sarikaya, T. Alpcan, and O. Ercetin 140Exchange Economy in Two-User Multiple-Input Single-Output Interference Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Mochaourab and E. A. Jorswieck 151Intervention in Power Control Games With Selfish Users . . . . . . . . . . . . .. . . . . . . . . . . . . Y. Xiao, J. Park, and M. van der Schaar 165Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Xu, J. Wang, Q. Wu, A. Anpalagan, and Y.-D. Yao 180Coalitional Games in Partition Form for Joint Spectrum Sensing and Access in Cognitive Radio Networks . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Saad, Z. Han, R. Zheng, A. Hjørungnes, T. Basar, and H. V. Poor 195

Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210


CALL FOR PAPERS IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

Special Issue on Privacy and Trust Management in Cloud and Distributed Systems

Guest Editors Karl Aberer, École Polytechnique Fédérale de Lausanne, Switzerland ([email protected]) Sen-ching Samson Cheung, University of Kentucky, USA ([email protected]) Jayant Haritsa Indian Institute of Science, India ([email protected]) Bill Horne Hewlett-Packard Laboratories, USA ([email protected]) Kai Hwang University of Southern California, USA ([email protected]) Yan (Lindsay) Sun University of Rhode Island, USA ([email protected]) With the increasing drive towards availability of data and services anytime anywhere, privacy risks have significantly increased. Unauthorized disclosure, modification, usage, or uncontrolled access to privacy-sensitive data may result in high human and financial costs. In the distributed computing environments, trust plays a crucial role in mitigating the privacy risk by guaranteeing meaningful interactions, data sharing, and communications. Trust management is a key enabling technology for security and privacy enhancement. While privacy preservation and trust management are already challenging problems, it is imperative to explore how privacy-oriented and trust-oriented approaches can integrate to bring new solutions in safeguarding information sharing and protecting critical cyber infrastructure. Furthermore, there are questions about whether existing trust models and privacy preserving schemes are robust against attacks. This Call for Papers invites researchers to contribute original articles that cover a broad range of topics related to privacy preservation and trust management in cloud and distributed systems, with a focus on emerging networking contexts such as social media, cloud computing, and power grid systems.

Example topics include but are not limited to

Privacy Enhanced Technology: privacy preserving data mining, publishing, and disclosure; access control, anonymity, audit, and authentication; applied cryptography, cryptanalysis, and digital signatures in PET; abuse cases and threat modeling; theoretical models and formal methods; application of physical security for privacy enhancement.

Trust and Reputation Management: trust management architectures and trust models; quantitative metrics and computation; security of trust management protocols/systems; evaluation and test bed; trust related privacy enhancement solutions.

Privacy and Trust in Emerging Complex Systems including: social networking; cloud computing; power grid systems; sensor networks; Internet of Things; multimedia surveillance networks.

Other Related Topics such as trust and privacy policies; human factors and usability; censorship; economics of trust and privacy; behavior modeling.

Submission Procedure: Manuscripts are to be submitted according to the Information for Authors at http://www.signalprocessingsociety.org/publications/periodicals/forensics/forensics-authors-info/ using the IEEE online manuscript system, Manuscript Central. Papers must not have appeared or be under review elsewhere.

Schedule Submission deadline: May 31, 2012 First Review: September 12, 2012 Revisions Due: October 31, 2012 Final Decision: January 15, 2013 Final manuscript due: February 20, 2013 Tentative publication date: June 1, 2013


APRIL 2012 VOLUME 19 NUMBER 4 ISPLEM (ISSN 1070-9908)

LETTERS

Digital and Multirate Signal ProcessingIs Uniqueness Lost for Under-Sampled Continuous-Time Auto-Regressive Processes? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. P. Ward, H. Kirshner, and M. Unser 183

Image and Multidimensional Signal ProcessingImproved Principal Component Regression for Face Recognition Under Illumination Variations . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S.-M. Huang and J.-F. Yang 179

Bidimensional Statistical Empirical Mode Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Kim, M. Park, and H.-S. Oh 191Super-Resolution Method for Multiview Face Recognition From a Single Image Per Person Using Nonlinear Mappingson Coherent Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Zeng and H. Huang 195

An Improved Hybrid Model for Automatic Salient Region Detection . . . . . . . . . . . . .. . . . . . . . . . . . . S. Liu, D. He, and X. Liang 207Region Diversity Maximization for Salient Object Detection . . . . . . . . . . . . . . . . R. Shi, Z. Liu, H. Du, X. Zhang, and L. Shen 215Robust Online Digital Image Stabilization Based on Point-Feature Trajectory Without Accumulative Global MotionEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. G. Ryu and M. J. Chung 223

(Contents Continued on Page 174)IEEE SIGNAL PROCESSING LETTERS (ISSN 1070–9908) is published quarterly in print and monthly online by the Institute of Electrical and Electronics Engineers, Inc. Responsibilityfor the contents rests upon the authors and not upon the IEEE, the Society/Council, or its members. IEEE Corporate Ofce: 3 Park Avenue, 17th Floor, New York, NY 10016-5997.IEEE Operations Center: 445 Hoes Lane, Piscataway, NJ 08854-4141. NJ Telephone: +1 732 981 0060. Price/Publication Information: Individual copies: IEEE Members $20.00(rst copy only), nonmembers $223.00 per copy. (Note: Postage and handling charge not included.) Member and nonmember subscription prices available upon request. Available inmicroche and microlm. Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy for private use of patrons,provided the per-copy fee indicated in the code at the bottom of the rst page is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For all othercopying, reprint, or republication permission, write to Copyrights and Permissions Department, IEEE Publications Administration, 445 Hoes Lane, Piscataway, NJ 08854-4141. Copyright© 2012 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Periodicals Postage pending at New York, NY and at additional mailing ofces. Postmaster:Send address changes to IEEE SIGNAL PROCESSING LETTERS, IEEE, 445 Hoes Lane, Piscataway, NJ 08854-4141. GST Registration No. 125634188. CPC Sales Agreement #40013087.Return undeliverable Canada addresses to: Pitney Bowes IMEX, P.O. Box 4332, Stanton Rd., Toronto, ON M5W 3J4, Canada. IEEE prohibits discrimination, harassment and bullying.For more information visit http://www.ieee.org/nondiscrimination. Printed in U.S.A.



Multimedia Signal ProcessingAn Improved Reversible Data Hiding in Encrypted Images Using Side Match . . . . . . . W. Hong, T.-S. Chen, and H.-Y. Wu 199Signal Processing for Communications

: Relative Referenceless Receiver/Receiver Time Synchronization in Wireless Sensor Networks . . . . D. Djenouri 175Performance Analysis of Adaptive Modulation in Multiuser Selection Diversity Systems With OSTBC OverTime-Varying Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Torabi, J.-F. Frigon, and B. Sansò 211

Performance Analysis of Zero Forcing Crosstalk Canceler in Vectored VDSL2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. M. Zafaruddin, S. Prakriya, and S. Prasad 219

Speech ProcessingConversational Evaluation of Speech Bandwidth Extension Using a Mobile Handset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Pulakka, L. Laaksonen, V. Myllylä, Y. Yrttiaho, and P. Alku 203

Statistical and Adaptive Signal ProcessingWavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . U. Kamilov, E. Bostan, and M. Unser 187

Stereophonic Acoustic Echo Suppression Based on Wiener Filter in the Short-Time Fourier Transform Domain . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Yang, M. Wu, and J. Yang 227

Alternating Least-Squares for Low-Rank Matrix Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Zachariah, M. Sundin, M. Jansson, and S. Chatterjee 231

Inference in Hidden Markov Models with Explicit State Duration Distributions . . .. . M. Dewar, C. Wiggins, and F. Wood 235


IEEE SIGN

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LUME 29 N

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[VOLUME 29 NUMBER 3 MAY 2012]

www.signalprocessingsociety.org [18] MAY 2012 www.signalprocessingsociety.org [19] MAY 2012IEEE SIGNAL PROCESSING MAGAZINE [1] MAY 2012

[CONTENTS]

SCOPE: IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest. Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional facets of interests and concerns of the community. Its mission is to bring up-to-date, emerging and active technical developments, issues, and events to the research, educational, and professional communities. It is also the main Society communication platform addressing important issues concerning all members.

IEEE SIGNAL PROCESSING MAGAZINE (ISSN 1053-5888) (ISPREG) is published bimonthly by the Institute of Electrical and Electronics Engineers, Inc., 3 Park Avenue, 17th Floor, New York, NY 10016-5997 USA (+1 212 419 7900). Responsibility for the contents rests upon the authors and not the IEEE, the Society, or its members. Annual member subscriptions included in Society fee. Nonmember subscriptions available upon request. Individual copies: IEEE Members $20.00 (first copy only), non-members $157.00 per copy. Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limits of U.S. Copyright Law for private use of patrons: 1) those post-1977 articles that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA; 2) pre-1978 articles without fee. Instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. For all other copying, reprint, or republication per-mission, write to IEEE Service Center, 445 Hoes Lane, Piscataway, NJ 08854 USA. Copyright©2012 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Periodicals postage paid at New York, NY, and at additional mailing offices. Postmaster: Send address changes to IEEE Signal Processing Magazine, IEEE, 445 Hoes Lane, Piscataway, NJ 08854 USA. Canadian GST #125634188Printed in the U.S.A.

[VOLUME 29 NUMBER 3]

Digital Object Identifi er 10.1109/MSP.2012.2183493

[COVER] ©GETTY IMAGES/WESTEND61

13 FROM THE GUEST EDITORSFred Aminzadeh, Sven Treitel, and Mauricio Sacchi

16 SOURCE SEPARATION ON SEISMIC DATAAishwarya Moni, Christopher J. Bean, Ivan Lokmer, and Scott Rickard

29 MULTICOMPONENT SIGNAL PROCESSING FOR RAYLEIGH WAVE ELLIPTICITY ESTIMATIONManuel Hobiger, Nicolas Le Bihan, Cécile Cornou, and Pierre-Yves Bard

40 THE METHODOLOGY OF THE MAXIMUM LIKELIHOOD APPROACHPei-Jung Chung and Johann F. Böhme

47 SEISMIC COHERENCY MEASURES IN CASE OF INTERFERING EVENTS Endrias G. Asgedom, Leiv J. Gelius, and Martin Tygel

57 NUCLEAR TEST BAN TREATY VERIFICATIONDavid B. Harris, Steven J. Gibbons, Arthur J. Rodgers, and Michael E. Pasyanos

71 DIVIDE-AND-CONQUER STRATEGIES FOR HYPERSPECTRAL IMAGE PROCESSINGIan Blanes, Joan Serra-Sagristà, Michael W. Marcellin, and Joan Bartrina-Rapesta

82 FUZZY CLUSTERING OF SEISMIC SEQUENCESHosein Hashemi

88 FIGHTING THE CURSE OF DIMENSIONALITYFelix J. Herrmann, Michael P. Friedlander, and Özgür Yılmaz

[FEATURE] 101 SPECTRUM SENSING

FOR COGNITIVE RADIOErik Axell, Geert Leus, Erik G. Larsson, and H. Vincent Poor

[COLUMNS] 2 FROM THE EDITOR

Interdisciplinary Research: A Catalyst for InnovationAbdelhak Zoubir

6 PRESIDENT’S MESSAGEA Melting PotK.J. Ray Liu

8 SPECIAL REPORTSWireless Sensors Relay Medical Insight to Patients and CaregiversJohn Edwards

14 READER’S CHOICETop Downloads in IEEE Xplore

117 APPLICATIONS CORNERSeismic Migration: A Digital Filtering Process Reducing Oil Exploration RisksWail A. Mousa

124 LIFE SCIENCESGenomic Signal ProcessingEdward R. Dougherty

136 IN THE SPOTLIGHTDSP Applications in Electric and Hybrid Electric VehiclesBilal Akin, Seungdeog Choi, and Hamid A. Toliyat

[DEPARTMENT] 134 DATES AHEAD

[ SPECIAL SECTION—GEOPHYSICAL SIGNAL PROCESSING]

$

www.signalprocessingsociety.org [18] MAY 2012 www.signalprocessingsociety.org [19] MAY 2012IEEE SIGNAL PROCESSING MAGAZINE [1] MAY 2012

[CONTENTS]

SCOPE: IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest. Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional facets of interests and concerns of the community. Its mission is to bring up-to-date, emerging and active technical developments, issues, and events to the research, educational, and professional communities. It is also the main Society communication platform addressing important issues concerning all members.

IEEE SIGNAL PROCESSING MAGAZINE (ISSN 1053-5888) (ISPREG) is published bimonthly by the Institute of Electrical and Electronics Engineers, Inc., 3 Park Avenue, 17th Floor, New York, NY 10016-5997 USA (+1 212 419 7900). Responsibility for the contents rests upon the authors and not the IEEE, the Society, or its members. Annual member subscriptions included in Society fee. Nonmember subscriptions available upon request. Individual copies: IEEE Members $20.00 (first copy only), non-members $157.00 per copy. Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limits of U.S. Copyright Law for private use of patrons: 1) those post-1977 articles that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA; 2) pre-1978 articles without fee. Instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. For all other copying, reprint, or republication per-mission, write to IEEE Service Center, 445 Hoes Lane, Piscataway, NJ 08854 USA. Copyright©2012 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Periodicals postage paid at New York, NY, and at additional mailing offices. Postmaster: Send address changes to IEEE Signal Processing Magazine, IEEE, 445 Hoes Lane, Piscataway, NJ 08854 USA. Canadian GST #125634188Printed in the U.S.A.

[VOLUME 29 NUMBER 3]

Digital Object Identifi er 10.1109/MSP.2012.2183493

[COVER] ©GETTY IMAGES/WESTEND61

13 FROM THE GUEST EDITORSFred Aminzadeh, Sven Treitel, and Mauricio Sacchi

16 SOURCE SEPARATION ON SEISMIC DATAAishwarya Moni, Christopher J. Bean, Ivan Lokmer, and Scott Rickard

29 MULTICOMPONENT SIGNAL PROCESSING FOR RAYLEIGH WAVE ELLIPTICITY ESTIMATIONManuel Hobiger, Nicolas Le Bihan, Cécile Cornou, and Pierre-Yves Bard

40 THE METHODOLOGY OF THE MAXIMUM LIKELIHOOD APPROACHPei-Jung Chung and Johann F. Böhme

47 SEISMIC COHERENCY MEASURES IN CASE OF INTERFERING EVENTS Endrias G. Asgedom, Leiv J. Gelius, and Martin Tygel

57 NUCLEAR TEST BAN TREATY VERIFICATIONDavid B. Harris, Steven J. Gibbons, Arthur J. Rodgers, and Michael E. Pasyanos

71 DIVIDE-AND-CONQUER STRATEGIES FOR HYPERSPECTRAL IMAGE PROCESSINGIan Blanes, Joan Serra-Sagristà, Michael W. Marcellin, and Joan Bartrina-Rapesta

82 FUZZY CLUSTERING OF SEISMIC SEQUENCESHosein Hashemi

88 FIGHTING THE CURSE OF DIMENSIONALITYFelix J. Herrmann, Michael P. Friedlander, and Özgür Yılmaz

[FEATURE] 101 SPECTRUM SENSING

FOR COGNITIVE RADIOErik Axell, Geert Leus, Erik G. Larsson, and H. Vincent Poor

[COLUMNS] 2 FROM THE EDITOR

Interdisciplinary Research: A Catalyst for InnovationAbdelhak Zoubir

6 PRESIDENT’S MESSAGEA Melting PotK.J. Ray Liu

8 SPECIAL REPORTSWireless Sensors Relay Medical Insight to Patients and CaregiversJohn Edwards

14 READER’S CHOICETop Downloads in IEEE Xplore

117 APPLICATIONS CORNERSeismic Migration: A Digital Filtering Process Reducing Oil Exploration RisksWail A. Mousa

124 LIFE SCIENCESGenomic Signal ProcessingEdward R. Dougherty

136 IN THE SPOTLIGHTDSP Applications in Electric and Hybrid Electric VehiclesBilal Akin, Seungdeog Choi, and Hamid A. Toliyat

[DEPARTMENT] 134 DATES AHEAD

[ SPECIAL SECTION—GEOPHYSICAL SIGNAL PROCESSING]

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IEEE Multimedia Signal Processing (MMSP) Workshop 2014

Invitation to Submit Proposals

Detail about the MMSP workshops can be found in http://www.signalprocessingsociety.org/technical-committees/list/mmsp-tc/conferences-workshops/

The Multimedia Signal Processing Technical Committee (MMSP-TC) of the

IEEE Signal Processing Society, which is the organizer of the MMSP

workshop, invites proposals for the 2014 workshop edition. The primary

aim of the MMSP workshop is to promote the advancement of multimedia

signal processing research and technology with special emphasis on the

interaction, coordination, synchronization, and joint processing of

multimodal signals.

Proposals are open to all regions, but to keep with the practice of regional

rotation, preference will be given to proposals from Region 10. The

proponents should first send an intention letter prior to the proposal

submission, and then submit their proposal electronically to MMSP-TC

Chair, Prof. Oscar Au at [email protected] by the deadlines indicated below.

Proposals should address the following content, format and scheduling

requirements. Proponents are strongly encouraged to present their

proposal at the TC meeting to be held during MMSP 2012 in Banff, Canada

Content:

Propose a well-defined theme for the workshop

The theme should reflect emerging and interdisciplinary topics that are

relevant to the multimedia signal processing community.

The theme should emphasize the multimodal aspects, or the aspects that

are applicable to multiple modalities, rather than single modal aspects of

signal processing.

The theme should be well defined, sufficiently specialized to create focus,

and of significant interest to the MMSP community at large. Examples

may be found from the web pages of recent MMSP workshops.

The proponents are encouraged to propose a single theme although in

some instances two themes may be appropriate as long as each covers

both oral and poster sessions.

The proposed theme will be evaluated and possibly refined by the

MMSP-TC.

List the planned technical program components

The proponents are encouraged to include in the planned technical

program: keynote talks, overview talks and panels. Special sessions and

demo sessions are desired but optional.

Format: Propose a clear format of the workshop.

The proponents are asked to suggest a three-day format. The format

should combine focused theme topics with general topics of interest to

the MMSP audience. Examples of formats include: one day allocated for

the focused theme and one day allocated for general MMSP topics; or

interleaving the topics over three days.

Proposals for other format solutions with sound rationale are also

welcome.

Team: Propose an organizing team of the workshop.

The proponents are asked to provide a list of key organizing committee

members. The organizing committee should be international and should

have proven experience in organizing events like MMSP or other

international conferences.

Inclusion of at least one current or past member of the MMSP TC in the

key organizing committee members, perhaps as technical program

co-chair, is encouraged.

Scheduling, Location and Costs: Propose a clear scheduling of the

MMSP workshop

Proposals for MMSP should take into account the accessibility of

workshop location. The characteristics of the venue and costs associated

should be included. Inclusion of a preliminary budget is encouraged.

Paper Review: Propose solutions to bring in additional reviewers outside

the MMSP-TC.

The MMSP TC members are responsible for reviewing the papers

submitted to the MMSP workshop.

A system wherein there are two or more primary reviewers for each

paper, with a TC member serving as one of the reviewers, is desired.

The proposal should estimate how many reviewers will be needed, and

indicate how and how many additional good quality reviewers outside the

MMSP TC will be recruited.

The proponents are expected to build a database of reliable and good

quality reviewers, which can be enhanced upon the previous years’

reviewer base. The final database of reviewers, authors, and attendees

should be turned in to the MMSP-TC at the conclusion of the workshop.

Timetable for Proposal Submission and Evaluation:

Intention letter of proponents to the TC Chair: July 1, 2012

Proposal submission deadline: August 15, 2012

Evaluation of proposals by MMSP-TC: September 17 19, 2012 (MMSP

TC Meeting during MMSP 2012)

Notification of decision to proponents: September 30, 2012


IEEE Transactions on Multimedia 2012Prize Paper Award

The nominations period for the IEEE Transactions on Multimedia 2012 Prize Paper Award is open!

Any paper published in T-MM in 2009, 2010, or 2011 is eligible.

Nominations should include:1. Nominator2. Bibliographic information for the paper3. A short statement of support (less than 500 words) explaining the rationale for the nomination.

Judging shall be on the bases of general quality, originality, subject matter, and timeliness.

Nominations should be sent to the T-MM Editor-in-Chief Mihaela van der Schaar ([email protected]) no later than 30-May-2012.




ICASSP 20132013 IEEE International Conference on Acoustics,

Speech, and Signal Processing (ICASSP) Vancouver Convention & Exhibition Centre

May 26 - 31, 2013 • Vancouver, Canada

www.ICASSP2013.com

General ChairsRabab Ward University of British Columbia

Li DengMicrosoft

Technical Program ChairsVikram KrishnamurthyUniversity of British Columbia

Kostas PlataniotisUniversity of Toronto

Finance ChairJane WangUniversity of British Columbia

Special Sessions ChairsXiaodong HeMicrosoft

Wu ChouHuawei

Tutorials ChairKhaled El-MalehQualcomm

Local Arrangement ChairPanos Nasiopoulos University of British Columbia

Social Program ChairRabab WardUniversity of British Columbia

Webmaster ChairMehrdad Fatourechi University of British Columbia

Publicity ChairsLina KaramArizona State University

Michel Sarkis Sony

Publication ChairsMichael AdamsUniversity of Victoria

Vicky ZhaoUniversity of AlbertaExhibit ChairDong YuMicrosoft

Wu ChouHuawei

Entrepreneurial RelationshipTon KalkerHuawei

Conference ManagementBillene Mercer Conference Management Services

The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) will be held in the Vancouver Convention & Exhibition Centre, Vancouver, Canada, on May 26 - 31, 2013. ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 120 lecture and poster sessions. Topics include but are not limited to:

Vancouver: Vancouver is one of the most beautiful cities in the world. It is surrounded by dense pine forests, snow-capped mountains and fjords. The ocean and the mountains surround the city while the expanses of the trees cover it. It is a city with vast beaches and lush parks combined with magnificent architecture. Vancouver is the place to enjoy the golden era of signal processing as our field (as well as to explore British Columbia) and is waiting to welcome you and your family.

Submission of Papers: Prospective authors are invited to submit full-length papers. The length of the paper may be changed from past years.The ICASSP 2013 website www.icassp2013.com will provide you with further details. A selection of best papers will be made by the ICASSP 2013 committee upon recommendations from Technical Committees.

Notice: The IEEE Signal Processing Society enforces a “no-show” policy. Any accepted paper included in the final program is expected to have at least one author or qualified proxy attend and present the paper at the conference. Authors of the accepted papers included in the final program who do not attend the conference will be subscribed to a "No-Show List", compiled by the Society. The "no-show" papers will not be published by IEEE on IEEEXplore or other public access forums, but these papers will be distributed as part of the on-site electronic proceedings and the copyright of these papers will belong to the IEEE.

Tutorial and Special Sessions Proposals: Tutorials will be held on May 26 and 27, 2013. Brief proposals should be submitted by August 31, 2012, to [email protected] and must include title, outline, contact information, biography and selected publications for the presenter(s), and a description of the tutorial and material to be distributed to participants. Special sessions proposals should be submitted by August 31, 2012, to [email protected] and must include a topical title, rationale, session outline, contact information, and a list of invited papers. Refer to the ICASSP 2013 website for additional information.

Important Deadlines: Special Session & Tutorial Proposals Due...................................................... August 31, 2012Notification of Special Session & Tutorial Acceptance.................................. October 1, 2012Submission of Regular Papers......................................................................... November 19, 2012Notification of Paper Acceptance.................................................................... February 18, 2013Revised Paper Upload Deadline...................................................................... March 18, 2013Author's Registration Deadline........................................................................ March 18, 2013

Audio and acoustic signal processingBio- imaging and signal processingSignal processing educationSpeech processing Industry technology tracksInformation forensics and securityMachine learning for signal processing

Multimedia signal processingSensor array & multichannel signal processingDesign & implementation of signal processing systemsSignal processing for communications & networkingImage, video & multidimensional signal processingSignal processing theory & methodsSpoken language processing

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Discovering Thematic Objects in Image Collections and Videos