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Image Processing, IEEE Transactions on

Issue 11 • Date Nov. 2004

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Displaying Results 1 - 16 of 16
  • Table of contents

    Publication Year: 2004 , Page(s): c1 - c4
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  • IEEE Transactions on Image Processing publication information

    Publication Year: 2004 , Page(s): c2
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  • A survey on palette reordering methods for improving the compression of color-indexed images

    Publication Year: 2004 , Page(s): 1411 - 1418
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (807 KB) |  | HTML iconHTML  

    Palette reordering is a well-known and very effective approach for improving the compression of color-indexed images. In this paper, we provide a survey of palette reordering methods, and we give experimental results comparing the ability of seven of them in improving the compression efficiency of JPEG-LS and lossless JPEG 2000. We concluded that the pairwise merging heuristic proposed by Memon et al. is the most effective, but also the most computationally demanding. Moreover, we found that the second most effective method is a modified version of Zeng's reordering technique, which was 3%-5% worse than pairwise merging, but much faster. View full abstract»

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  • An efficient Re-indexing algorithm for color-mapped images

    Publication Year: 2004 , Page(s): 1419 - 1423
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (455 KB) |  | HTML iconHTML  

    The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio. View full abstract»

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  • Improved structures of maximally decimated directional filter Banks for spatial image analysis

    Publication Year: 2004 , Page(s): 1424 - 1431
    Cited by:  Papers (37)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1239 KB) |  | HTML iconHTML  

    This paper introduces an improved structure for directional filter banks (DFBs) that preserves the visual information in the subband domain. The new structure achieves this outcome while preserving both the efficient polyphase implementation and the exact reconstruction property. The paper outlines a step-by-step framework in which to examine the DFB, and within this framework discusses how, through the insertion of post-sampling matrices, visual distortions can be removed. In addition to the efficient tree structure, attention is given to the form and design of efficient linear phase filters. Most notably, linear phase IIR prototype filters are presented, together with the design details. These filters can enable the DFB to have more than a three-fold improvement in complexity reduction over quadrature mirror filters (QMFs). View full abstract»

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  • Dense motion estimation using regularization constraints on local parametric models

    Publication Year: 2004 , Page(s): 1432 - 1443
    Cited by:  Papers (2)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2352 KB) |  | HTML iconHTML  

    This paper presents a method for dense optical flow estimation in which the motion field within patches that result from an initial intensity segmentation is parametrized with models of different order. We propose a novel formulation which introduces regularization constraints between the model parameters of neighboring patches. In this way, we provide the additional constraints for very small patches and for patches whose intensity variation cannot sufficiently constrain the estimation of their motion parameters. In order to preserve motion discontinuities, we use robust functions as a regularization mean. We adopt a three-frame approach and control the balance between the backward and forward constraints by a real-valued direction field on which regularization constraints are applied. An iterative deterministic relaxation method is employed in order to solve the corresponding optimization problem. Experimental results show that the proposed method deals successfully with motions large in magnitude, motion discontinuities, and produces accurate piecewise-smooth motion fields. View full abstract»

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  • A regularized curvature flow designed for a selective shape restoration

    Publication Year: 2004 , Page(s): 1444 - 1458
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3259 KB) |  | HTML iconHTML  

    Among all filtering techniques, those based exclusively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves nontrivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization. View full abstract»

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  • Statistical modeling of complex backgrounds for foreground object detection

    Publication Year: 2004 , Page(s): 1459 - 1472
    Cited by:  Papers (233)  |  Patents (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2738 KB) |  | HTML iconHTML  

    This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features , at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results. View full abstract»

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  • Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensation

    Publication Year: 2004 , Page(s): 1473 - 1490
    Cited by:  Papers (9)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6033 KB) |  | HTML iconHTML  

    The purpose of this study is to investigate a method of tracking moving objects with a moving camera. This method estimates simultaneously the motion induced by camera movement. The problem is formulated as a Bayesian motion-based partitioning problem in the spatiotemporal domain of the image sequence. An energy functional is derived from the Bayesian formulation. The Euler-Lagrange descent equations determine simultaneously an estimate of the image motion field induced by camera motion and an estimate of the spatiotemporal motion boundary surface. The Euler-Lagrange equation corresponding to the surface is expressed as a level-set partial differential equation for topology independence and numerically stable implementation. The method can be initialized simply and can track multiple objects with nonsimultaneous motions. Velocities on motion boundaries can be estimated from geometrical properties of the motion boundary. Several examples of experimental verification are given using synthetic and real-image sequences. View full abstract»

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  • Visual tracking and recognition using appearance-adaptive models in particle filters

    Publication Year: 2004 , Page(s): 1491 - 1506
    Cited by:  Papers (231)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4960 KB) |  | HTML iconHTML  

    We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations. View full abstract»

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  • Polygonal and polyhedral contour reconstruction in computed tomography

    Publication Year: 2004 , Page(s): 1507 - 1523
    Cited by:  Papers (10)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1193 KB) |  | HTML iconHTML  

    This paper is about three-dimensional (3-D) reconstruction of a binary image from its X-ray tomographic data. We study the special case of a compact uniform polyhedron totally included in a uniform background and directly perform the polyhedral surface estimation. We formulate this problem as a nonlinear inverse problem using the Bayesian framework. Vertice estimation is done without using a voxel approximation of the 3-D image. It is based on the construction and optimization of a regularized criterion that accounts for surface smoothness. We investigate original deterministic local algorithms, based on the exact computation of the line projections, their update, and their derivatives with respect to the vertice coordinates. Results are first derived in the two-dimensional (2-D) case, which consists of reconstructing a 2-D object of deformable polygonal contour from its tomographic data. Then, we investigate the 3-D extension that requires technical adaptations. Simulation results illustrate the performance of polygonal and polyhedral reconstruction algorithms in terms of quality and computation time. View full abstract»

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  • A multiplicative regularization approach for deblurring problems

    Publication Year: 2004 , Page(s): 1524 - 1532
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB) |  | HTML iconHTML  

    In this work, an iterative inversion algorithm for deblurring and deconvolution is considered. The algorithm is based on the conjugate gradient scheme and uses the so-called weighted L2-norm regularizer to obtain a reliable solution. The regularizer is included as a multiplicative constraint. In this way, the appropriate regularization parameter will be controlled by the optimization process itself. In fact, the misfit in the error in the space of the blurring operator is the regularization parameter. Then, no a priori knowledge on the blurred data or image is needed. If noise is present, the misfit in the error consisting of the blurring operator will remain at a large value during the optimization process; therefore, the weight of the regularization factor will be more significant. Hence, the noise will, at all times, be suppressed in the reconstruction process. Although one may argue that, by including the regularization factor as a multiplicative constraint, the linearity of the problem has been lost, careful analysis shows that, under certain restrictions, no new local minima are introduced. Numerical testing shows that the proposed algorithm works effectively and efficiently in various practical applications. View full abstract»

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  • Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application

    Publication Year: 2004 , Page(s): 1533 - 1543
    Cited by:  Papers (58)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1272 KB) |  | HTML iconHTML  

    This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases. View full abstract»

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  • IEEE Transactions on Image Processing Edics

    Publication Year: 2004 , Page(s): 1544
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  • IEEE Transactions on Image Processing Information for authors

    Publication Year: 2004 , Page(s): 1545 - 1546
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  • IEEE Signal Processing Society Information

    Publication Year: 2004 , Page(s): c3
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Aims & Scope

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

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Meet Our Editors

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