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Image Processing, IEEE Transactions on

Issue 8 • Date Aug. 2009

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Displaying Results 1 - 25 of 31
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE
  • Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

    Page(s): 1689 - 1702
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2601 KB) |  | HTML iconHTML  

    We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian scale mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However, the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods. View full abstract»

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  • Geometric Video Approximation Using Weighted Matching Pursuit

    Page(s): 1703 - 1716
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    In recent years, works on geometric multidimensional signal representations have established a close relation with signal expansions on redundant dictionaries. For this purpose, matching pursuits (MP) have shown to be an interesting tool. Recently, most important limitations of MP have been underlined, and alternative algorithms like weighted-MP have been proposed. This work explores the use of weighted-MP as a new framework for motion-adaptive geometric video approximations. We study a novel algorithm to decompose video sequences in terms of few, salient video components that jointly represent the geometric and motion content of a scene. Experimental coding results on highly geometric content reflect how the proposed paradigm exploits spatio-temporal video geometry. Two-dimensional weighted-MP improves the representation compared to those based on 2-D MP. Furthermore, the extracted video components represent relevant visual structures with high saliency. In an example application, such components are effectively used as video descriptors for the joint audio-video analysis of multimedia sequences. View full abstract»

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  • Efficient Block-Based Frequency Domain Wavelet Transform Implementations

    Page(s): 1717 - 1723
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (378 KB) |  | HTML iconHTML  

    Subband decompositions for image coding have been explored extensively over the last few decades. The condensed wavelet packet (CWP) transform is one such decomposition that was recently shown to have coding performance advantages over conventional decompositions. A special feature of the CWP is that its design and implementation are performed in the cyclic frequency domain. While performance gains have been reported, efficient implementations of the CWP (or more generally, efficient implementations of cyclic filter banks) have not yet been fully explored. In this paper, we present efficient block-based implementations of cyclic filter banks along with an analysis of the arithmetic complexity. Block-based cyclic filter bank implementations of the CWP coder are compared with conventional subband/wavelet image coders whose filter banks are implemented in the time domain. It is shown that block-based cyclic filter bank implementations can result in CWP coding systems that outperform the popular image coding systems both in terms of arithmetic complexity and coding performance. View full abstract»

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  • Bayesian Inference on Multiscale Models for Poisson Intensity Estimation: Applications to Photon-Limited Image Denoising

    Page(s): 1724 - 1741
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3680 KB) |  | HTML iconHTML  

    We present an improved statistical model for analyzing Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main contributions include: 1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the noisy observed Poisson data (counts); 2) extension of the method to work under a multiscale hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling interscale coefficient dependencies in the vicinity of image edges; 3) exploration of a 2-D recursive quad-tree image representation, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary-tree image representation involving beta-mixture rate-ratio densities; and 4) a novel multiscale image representation, which we term Poisson-Haar decomposition, that better models the image edge structure, thus yielding improved performance. Experimental results on standard images with artificially simulated Poisson noise and on real photon-limited images demonstrate the effectiveness of the proposed techniques. View full abstract»

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  • Geometric Features-Based Filtering for Suppression of Impulse Noise in Color Images

    Page(s): 1742 - 1759
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2645 KB) |  | HTML iconHTML  

    A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity. View full abstract»

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  • A Connectivity-Based Method for Defining Regions-of-Interest in fMRI Data

    Page(s): 1760 - 1771
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1793 KB) |  | HTML iconHTML  

    In this paper, we describe a new methodology for defining brain regions-of-interset (ROIs) in functional magnetic resonance imaging (fMRI) data. The ROIs are defined based on their functional connectivity to other ROIs, i.e., ROIs are defined as sets of voxels with similar connectivity patterns to other ROIs. The method relies on 1) a spatially regularized canonical correlation analysis for identifying maximally correlated signals, which are not due to correlated noise; 2) a test for merging ROIs which have similar connectivity patterns to the other ROIs; and 3) a graph-cuts optimization for assigning voxels to ROIs. Since our method is fully connectivity-based, the extracted ROIs and their corresponding time signals are ideally suited for a subsequent brain connectivity analysis. View full abstract»

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  • Distortion Estimators for Bitplane Image Coding

    Page(s): 1772 - 1781
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1138 KB) |  | HTML iconHTML  

    Bitplane coding is a common strategy used in current image coding systems to perform lossy, or lossy-to-lossless, compression. There exist several studies and applications employing bitplane coding that require estimators to approximate the distortion produced when data are successively coded and transmitted. Such estimators usually assume that coefficients are uniformly distributed in the quantization interval. Even though this assumption simplifies estimation, it does not exactly correspond with the nature of the signal. This work introduces new estimators to approximate the distortion produced by the successive coding of transform coefficients in bitplane image coders, which have been determined through a precise approximation of the coefficients' distribution within the quantization intervals. Experimental results obtained in three applications suggest that the proposed estimators are able to approximate distortion with very high accuracy, providing a significant improvement over state-of-the-art results. View full abstract»

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  • A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss–Hermite Expansion

    Page(s): 1782 - 1796
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2040 KB) |  | HTML iconHTML  

    Traditional statistical detectors of the discrete wavelet transform (DWT)-based image watermarking use probability density functions (PDFs) that show inadequate matching with the empirical PDF of image coefficients in view o f the fact that they use a fixed number of parameters. Hence, the decision values obtained from the estimated thresholds of these detectors provide substandard detection performance. In this paper, a new detector is proposed for the DWT-based additive image watermarking, wherein a PDF based on the Gauss-Hermite expansion is used, in view of the fact that this PDF provides a better statistical match to the empirical PDF by utilizing an appropriate number of parameters estimated from higher-order moments of the image coefficients. The decision threshold and the receiver operating characteristics are derived for the proposed detector. Experimental results on test images demonstrate that the proposed watermark detector performs better than other standard detectors such as the Gaussian and generalized Gaussian (GG), in terms of the probabilities of detection and false alarm as well as the efficacy. It is also shown that detection performance of the proposed detector is more robust than the competitive GG detector in the case of compression, additive white Gaussian noise, filtering, or geometric attack. View full abstract»

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  • A High-Capacity Steganography Scheme for JPEG2000 Baseline System

    Page(s): 1797 - 1803
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1530 KB) |  | HTML iconHTML  

    Hiding capacity is very important for efficient covert communications. For JPEG2000 compressed images, it is necessary to enlarge the hiding capacity because the available redundancy is very limited. In addition, the bitstream truncation makes it difficult to hide information. In this paper, a high-capacity steganography scheme is proposed for the JPEG2000 baseline system, which uses bit-plane encoding procedure twice to solve the problem due to bitstream truncation. Moreover, embedding points and their intensity are determined in a well defined quantitative manner via redundancy evaluation to increase hiding capacity. The redundancy is measured by bit, which is different from conventional methods which adjust the embedding intensity by multiplying a visual masking factor. High volumetric data is embedded into bit-planes as low as possible to keep message integrality, but at the cost of an extra bit-plane encoding procedure and slightly changed compression ratio. The proposed method can be easily integrated into the JPEG2000 image coder, and the produced stego-bitstream can be decoded normally. Simulation shows that the proposed method is feasible, effective, and secure. View full abstract»

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  • Improved Dot Diffusion by Diffused Matrix and Class Matrix Co-Optimization

    Page(s): 1804 - 1816
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4289 KB) |  | HTML iconHTML  

    Dot diffusion is an efficient approach which utilizes concepts of block-wise and parallel-oriented processing to generate halftones. However, the block-wise nature of processing reduces image quality much more significantly as compared to error diffusion. In this work, four types of filters with various sizes are employed in co-optimization procedures with class matrices of size 8 times 8 and 16 times 16 to improve the image quality. The optimal diffused weighting and area are determined through simulations. Many well-known halftoning methods, some of which includes direct binary search (DBS), error diffusion, ordered dithering, and prior dot diffusion methods, are also included for comparisons. Experimental results show that the proposed dot diffusion achieved quality close to some forms of error diffusion, and additionally, superior to the well-known Jarvis and Stucki error diffusion and Mese's dot diffusion. Moreover, the inherent parallel processing advantage of dot diffusion is preserved, allowing us to reap higher executing efficiency than both DBS and error diffusion. View full abstract»

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  • Active Mask Segmentation of Fluorescence Microscope Images

    Page(s): 1817 - 1829
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1629 KB) |  | HTML iconHTML  

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the ldquocontourrdquo to that of ldquoinside and outside,rdquo or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively. View full abstract»

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  • Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

    Page(s): 1830 - 1843
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1605 KB) |  | HTML iconHTML  

    In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ldquofragmentationrdquo step allows one to find the elementary textures of the model, while the ldquoreconstructionrdquo step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images. View full abstract»

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  • An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation

    Page(s): 1844 - 1858
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2924 KB) |  | HTML iconHTML  

    Centroidal Voronoi tessellations (CVTs) are special Voronoi tessellations whose generators are also the centers of mass (centroids) of the Voronoi regions with respect to a given density function and CVT-based methodologies have been proven to be very useful in many diverse applications in science and engineering. In the context of image processing and its simplest form, CVT-based algorithms reduce to the well-known k -means clustering and are easy to implement. In this paper, we develop an edge-weighted centroidal Voronoi tessellation (EWCVT) model for image segmentation and propose some efficient algorithms for its construction. Our EWCVT model can overcome some deficiencies possessed by the basic CVT model; in particular, the new model appropriately combines the image intensity information together with the length of cluster boundaries, and can handle very sophisticated situations. We demonstrate through extensive examples the efficiency, effectiveness, robustness, and flexibility of the proposed method. View full abstract»

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  • Stabilization of Parametric Active Contours Using a Tangential Redistribution Term

    Page(s): 1859 - 1872
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1494 KB) |  | HTML iconHTML  

    Depending on implementation, active contours have been classified as geometric or parametric active contours. Parametric contours, irrespective of representation, are known to suffer from the problem of irregular bunching and spacing out of curve points during the curve evolution. In a spline-based implementation of active contours, this leads to occasional formation of loops locally, and subsequently the curve blows up due to instabilities. In this paper, we analyze the reason for this problem and propose a solution to alleviate the same. We propose an ordinary differential equation (ODE) for controlling the curve parametrization during evolution by including a tangential force. We show that the solution of the proposed ODE is bounded. We demonstrate the effectiveness of the proposed method for segmentation and tracking tasks on closed as well as open contours. View full abstract»

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  • Learning Scene Context for Multiple Object Tracking

    Page(s): 1873 - 1884
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1738 KB) |  | HTML iconHTML  

    We propose a framework for multitarget tracking with feedback that accounts for scene contextual information. We demonstrate the framework on two types of context-dependent events, namely target births (i.e., objects entering the scene or reappearing after occlusion) and spatially persistent clutter. The spatial distributions of birth and clutter events are incrementally learned based on mixtures of Gaussians. The corresponding models are used by a probability hypothesis density (PHD) filter that spatially modulates its strength based on the learned contextual information. Experimental results on a large video surveillance dataset using a standard evaluation protocol show that the feedback improves the tracking accuracy from 9% to 14% by reducing the number of false detections and false trajectories. This performance improvement is achieved without increasing the computational complexity of the tracker. View full abstract»

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  • Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

    Page(s): 1885 - 1896
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1463 KB) |  | HTML iconHTML  

    In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol. View full abstract»

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  • Comments on "Phase-Shifting for Nonseparable 2-D Haar Wavelets

    Page(s): 1897 - 1898
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    In their recent paper, (see ibid., vol.17, no.7, p.1061-8, 2008) Alnasser and Foroosh derive a wavelet-domain (in-band) method for phase-shifting of 2-D ldquononseparablerdquo Haar transform coefficients. Their approach is parametrical to the (a priori known) image translation. In this correspondence, we show that the utilized transform is in fact the separable Haar discrete wavelet transform (DWT). As such, wavelet-domain phase shifting can be performed using previously-proposed phase-shifting approaches that utilize the overcomplete DWT (ODWT), if the given image translation is mapped to the phase component and in-band position within the ODWT. View full abstract»

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  • Super Resolution With Probabilistic Motion Estimation

    Page(s): 1899 - 1904
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2231 KB) |  | HTML iconHTML  

    Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that ultimately leads to the same algorithm as in our prior work [11]. The contribution of this paper is two-fold. First, the suggested approach is much simpler and more intuitive, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation. Second, the new approach offers various extensions not covered in our previous work, such as more general re-sampling tasks (e.g., de-interlacing). View full abstract»

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  • A Study on Gait-Based Gender Classification

    Page(s): 1905 - 1910
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (343 KB) |  | HTML iconHTML  

    Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Page(s): 1911
    Save to Project icon | Request Permissions | PDF file iconPDF (20 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing Information for authors

    Page(s): 1912 - 1913
    Save to Project icon | Request Permissions | PDF file iconPDF (46 KB)  
    Freely Available from IEEE
  • Special issue on distributed camera networks: sensing, processing, communication and computing

    Page(s): 1914
    Save to Project icon | Request Permissions | PDF file iconPDF (122 KB)  
    Freely Available from IEEE
  • Special issue on Processing Reverberant Speech Methodologies and Applications

    Page(s): 1915
    Save to Project icon | Request Permissions | PDF file iconPDF (136 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003