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Image Processing, IEEE Transactions on

Issue 5 • Date May 2004

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Displaying Results 1 - 17 of 17
  • Table of contents

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

    Page(s): c2
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  • Adaptive alpha-trimmed mean filters under deviations from assumed noise model

    Page(s): 627 - 639
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1066 KB) |  | HTML iconHTML  

    Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, α, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting α according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes. View full abstract»

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  • RAGS: region-aided geometric snake

    Page(s): 640 - 652
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1149 KB) |  | HTML iconHTML  

    An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the region-aided geometric snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images. View full abstract»

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  • Matching shapes with self-intersections:application to leaf classification

    Page(s): 653 - 661
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (361 KB) |  | HTML iconHTML  

    We address the problem of two-dimensional (2-D) shape representation and matching in presence of self-intersection for large image databases. This may occur when part of an object is hidden behind another part and results in a darker section in the gray level image of the object. The boundary contour of the object must include the boundary of this part which is entirely inside the outline of the object. The curvature scale space (CSS) image of a shape is a multiscale organization of its inflection points as it is smoothed. The CSS-based shape representation method has been selected for MPEG-7 standardization. We study the effects of contour self-intersection on the curvature scale space image. When there is no self-intersection, the CSS image contains several arch shape contours, each related to a concavity or a convexity of the shape. Self intersections create contours with minima as well as maxima in the CSS image. An efficient shape representation method has been introduced in this paper which describes a shape using the maxima as well as the minima of its CSS contours. This is a natural generalization of the conventional method which only includes the maxima of the CSS image contours. The conventional matching algorithm has also been modified to accommodate the new information about the minima. The method has been successfully used in a real world application to find, for an unknown leaf, similar classes from a database of classified leaf images representing different varieties of chrysanthemum. For many classes of leaves, self-intersection is inevitable during the scanning of the image. Therefore the original contributions of this paper is the generalization of the curvature scale space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves. View full abstract»

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  • Adaptive shape and texture intra refreshment schemes for improved error resilience in object-based video coding

    Page(s): 662 - 676
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop temporal error propagation when errors occur in the transmission or storage of the coded streams, since these errors may cause the decoded quality to decay very rapidly. In the context of object-based video coding, intra coding refreshment can be applied to both the shape and texture data. In this paper, novel shape and texture intra refreshment schemes are proposed which can be used by object-based video encoders, such as MPEG-4 video encoders, independently or combined. These schemes allow to adaptively determine when the shape and texture of the various video objects in a scene should be refreshed in order to maximize the decoded video quality for a certain total bit rate. View full abstract»

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  • Imaging below the diffraction limit: a statistical analysis

    Page(s): 677 - 689
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (358 KB)  

    The present paper is concerned with the statistical analysis of the resolution limit in a so-called "diffraction-limited" imaging system. The canonical case study is that of incoherent imaging of two closely-spaced sources of possibly unequal brightness. The objective is to study how far beyond the classical Rayleigh limit of resolution one can reach at a given signal to noise ratio. The analysis uses tools from statistical detection and estimation theory. Specifically, we will derive explicit relationships between the minimum detectable distance between two closely-spaced point sources imaged incoherently at a given SNR. For completeness, asymptotic performance analysis for the estimation of the unknown parameters is carried out using the Crame´r-Rao bound. To gain maximum intuition, the analysis is carried out in one dimension, but can be well extended to the two-dimensional case and to more practical models. View full abstract»

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  • Adaptively quadratic (AQua) image interpolation

    Page(s): 690 - 698
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (654 KB) |  | HTML iconHTML  

    Image interpolation is a key aspect of digital image processing. This paper presents a novel interpolation method based on optimal recovery and adaptively determining the quadratic signal class from the local image behavior. The advantages of the new interpolation method are the ability to interpolate directly by any factor and to model properties of the data acquisition system into the algorithm itself. Through comparisons with other algorithms it is shown that the new interpolation is not only mathematically optimal with respect to the underlying image model, but visually it is very efficient at reducing jagged edges, a place where most other interpolation algorithms fail. View full abstract»

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  • An efficient and effective region-based image retrieval framework

    Page(s): 699 - 709
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (355 KB) |  | HTML iconHTML  

    An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10 000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework. View full abstract»

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  • Linear interpolation revitalized

    Page(s): 710 - 719
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    We present a simple, original method to improve piecewise-linear interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the interpolation property. We determine the theoretical optimal shift that maximizes the quality of our shifted linear interpolation. Surprisingly enough, this optimal value is nonzero and close to 1/5. We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment. Both show a significant increase of the quality of the shifted method with respect to the standard one. We also observe that, in these results, we get a quality that is similar to that of the computationally more costly "high-quality" cubic convolution. View full abstract»

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  • Image registration by "Super-curves"

    Page(s): 720 - 732
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (881 KB)  

    We solve the 2-D affine image registration problem by curve matching and alignment. Our approach starts with a super-curve, which is formed by superimposing two affine related curves in one coordinate system. We use B-spline fusion technique to find a single B-spline approximation of the super-curve and a registration between the two curves simultaneously. This approach achieves superior accuracy and efficiency in curve matching and alignment. We then address the occlusion problem by finding partial match between the curves segmented using inflections and cusps, which are affine invariant. The combination of edge detection and curve alignment lead to accurate image registration. View full abstract»

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  • IEEE Transactions on Image Processing Edics

    Page(s): 733
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  • IEEE Transactions on Image Processing Information for authors

    Page(s): 734 - 735
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  • Special issue on supplement on secure media

    Page(s): 736
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  • Special issue on data mining of speech, audio and dialog

    Page(s): 737
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  • Special issue on speech-to-speech machine translation

    Page(s): 738
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  • IEEE Signal Processing Society Information

    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