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Image Processing, IEEE Transactions on

Issue 6 • Date June 2005

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Displaying Results 1 - 19 of 19
  • Table of contents

    Publication Year: 2005 , Page(s): c1 - c4
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  • IEEE Transactions on Image Processing publication information

    Publication Year: 2005 , Page(s): c2
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  • Learning multiview face subspaces and facial pose estimation using independent component analysis

    Publication Year: 2005 , Page(s): 705 - 712
    Cited by:  Papers (36)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2458 KB)  

    An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented. View full abstract»

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  • Reconstruction of nonuniformly sampled images in spline spaces

    Publication Year: 2005 , Page(s): 713 - 725
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1752 KB) |  | HTML iconHTML  

    This paper presents a novel approach to the reconstruction of images from nonuniformly spaced samples. This problem is often encountered in digital image processing applications. Nonrecursive video coding with motion compensation, spatiotemporal interpolation of video sequences, and generation of new views in multicamera systems are three possible applications. We propose a new reconstruction algorithm based on a spline model for images. We use regularization, since this is an ill-posed inverse problem. We minimize a cost function composed of two terms: one related to the approximation error and the other related to the smoothness of the modeling function. All the processing is carried out in the space of spline coefficients; this space is discrete, although the problem itself is of a continuous nature. The coefficients of regularization and approximation filters are computed exactly by using the explicit expressions of B-spline functions in the time domain. The regularization is carried out locally, while the computation of the regularization factor accounts for the structure of the nonuniform sampling grid. The linear system of equations obtained is solved iteratively. Our results show a very good performance in motion-compensated interpolation applications. View full abstract»

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  • Blind deconvolution of images using optimal sparse representations

    Publication Year: 2005 , Page(s): 726 - 736
    Cited by:  Papers (29)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2014 KB) |  | HTML iconHTML  

    The relative Newton algorithm, previously proposed for quasi-maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used as the nonlinear term for sparse sources. In addition, we propose a method of sparsification, which allows blind deconvolution of arbitrary sources, and show how to find optimal sparsifying transformations by supervised learning. View full abstract»

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  • Image reconstruction by linear programming

    Publication Year: 2005 , Page(s): 737 - 744
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (590 KB) |  | HTML iconHTML  

    One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by ℓ1-norm penalization and to update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be efficiently solved. In particular, one can apply the ν trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g., sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are also able to show the ν property for this extended LP leading to a method which is easy to use. Experimental results demonstrate the power of our approach. View full abstract»

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  • Fast incorporation of optical flow into active polygons

    Publication Year: 2005 , Page(s): 745 - 759
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4400 KB) |  | HTML iconHTML  

    In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features. View full abstract»

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  • Multidimensional orthogonal filter bank characterization and design using the Cayley transform

    Publication Year: 2005 , Page(s): 760 - 769
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (848 KB) |  | HTML iconHTML  

    We present a complete characterization and design of orthogonal infinite impulse response (IIR) and finite impulse response (FIR) filter banks in any dimension using the Cayley transform (CT). Traditional design methods for one-dimensional orthogonal filter banks cannot be extended to higher dimensions directly due to the lack of a multidimensional (MD) spectral factorization theorem. In the polyphase domain, orthogonal filter banks are equivalent to paraunitary matrices and lead to solving a set of nonlinear equations. The CT establishes a one-to-one mapping between paraunitary matrices and para-skew-Hermitian matrices. In contrast to the paraunitary condition, the para-skew-Hermitian condition amounts to linear constraints on the matrix entries which are much easier to solve. Based on this characterization, we propose efficient methods to design MD orthogonal filter banks and present new design results for both IIR and FIR cases. View full abstract»

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  • Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

    Publication Year: 2005 , Page(s): 770 - 782
    Cited by:  Papers (28)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2747 KB) |  | HTML iconHTML  

    The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery. View full abstract»

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  • Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms

    Publication Year: 2005 , Page(s): 783 - 795
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1622 KB) |  | HTML iconHTML  

    A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A k-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods. View full abstract»

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  • Space-dependent color gamut mapping: a variational approach

    Publication Year: 2005 , Page(s): 796 - 803
    Cited by:  Papers (19)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1367 KB) |  | HTML iconHTML  

    Gamut mapping deals with the need to adjust a color image to fit into the constrained color gamut of a given rendering medium. A typical use for this tool is the reproduction of a color image prior to its printing, such that it exploits best the given printer/medium color gamut, namely the colors the printer can produce on the given medium. Most of the classical gamut mapping methods involve a pixel-by-pixel mapping and ignore the spatial color configuration. Recently proposed spatial-dependent approaches for gamut mapping are either based on heuristic assumptions or involve a high computational cost. In this paper, we present a new variational approach for space-dependent gamut mapping. Our treatment starts with the presentation of a new measure for the problem, closely related to a recent measure proposed for Retinex. We also link our method to recent measures that attempt to couple spectral and spatial perceptual measures. It is shown that the gamut mapping problem leads to a quadratic programming formulation, guaranteed to have a unique solution if the gamut of the target device is convex. An efficient numerical solution is proposed with promising results. View full abstract»

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  • Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation

    Publication Year: 2005 , Page(s): 804 - 821
    Cited by:  Papers (70)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1314 KB) |  | HTML iconHTML  

    Digital fingerprinting is a method for protecting digital data in which fingerprints that are embedded in multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion, where several users combine their copies of the same content to attenuate/remove the original fingerprints. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We introduce the maximum detector and the thresholding detector for colluder identification. We then analyze the collusion resistance of a system to the averaging collusion attack for the performance criteria represented by the probability of a false negative and the probability of a false positive. Lower and upper bounds for the maximum number of colluders Kmax are derived. We then show that the detectors are robust to different collusion attacks. We further study different sets of performance criteria, and our results indicate that attacks based on a few dozen independent copies can confound such a fingerprinting system. We also propose a likelihood-based approach to estimate the number of colluders. Finally, we demonstrate the performance for detecting colluders through experiments using real images. View full abstract»

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  • Multipurpose image watermarking algorithm based on multistage vector quantization

    Publication Year: 2005 , Page(s): 822 - 831
    Cited by:  Papers (68)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2519 KB) |  | HTML iconHTML  

    The rapid growth of digital multimedia and Internet technologies has made copyright protection, copy protection, and integrity verification three important issues in the digital world. To solve these problems, the digital watermarking technique has been presented and widely researched. Traditional watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform, discrete Fourier transform (DFT), and discrete wavelet transform (DWT). Most of these algorithms are good for only one purpose. Recently, some multipurpose digital watermarking methods have been presented, which can achieve the goal of content authentication and copyright protection simultaneously. However, they are based on DWT or DFT. Lately, several robust watermarking schemes based on vector quantization (VQ) have been presented, but they can only be used for copyright protection. In this paper, we present a novel multipurpose digital image watermarking method based on the multistage vector quantizer structure, which can be applied to image authentication and copyright protection. In the proposed method, the semi-fragile watermark and the robust watermark are embedded in different VQ stages using different techniques, and both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility. View full abstract»

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  • Background learning for robust face recognition with PCA in the presence of clutter

    Publication Year: 2005 , Page(s): 832 - 843
    Cited by:  Papers (9)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4815 KB) |  | HTML iconHTML  

    We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms. View full abstract»

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  • IEEE Transactions on Image Processing Edics

    Publication Year: 2005 , Page(s): 844
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  • IEEE Transactions on Image Processing Information for authors

    Publication Year: 2005 , Page(s): 845 - 846
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  • 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'06)

    Publication Year: 2005 , Page(s): 847
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  • 2006 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006)

    Publication Year: 2005 , Page(s): 848
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  • IEEE Signal Processing Society Information

    Publication Year: 2005 , 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