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Image Processing, IEEE Transactions on

Issue 8 • Date Aug 2002

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Displaying Results 1 - 12 of 12
  • Fuzzy color histogram and its use in color image retrieval

    Page(s): 944 - 952
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (584 KB)  

    A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases. View full abstract»

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  • A random set view of texture classification

    Page(s): 859 - 867
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    Texture classification of an image or subimage is an important problem in texture analysis. Many procedures have been proposed. A global framework for texture classification based on random closed set theory is proposed in this paper. In this approach, a binary texture is considered as an outcome of a random closed set. Some distributional descriptors of this stochastic model are used as texture features in order to classify the binary texture, in particular spherical and linear contact distributions and K-functions. If a grayscale texture has to be classified, then the original texture is reduced to a multivariate random closed set where each component (a different random set) corresponds with those pixels verifying a local property. Again, some functional descriptors of the multivariate random closed set defined from the texture can be used as texture features to describe and classify the grayscale texture. Marginal and cross spherical and linear contact distributions and K-functions have been used. Experimental validation is provided by using Brodatz's database and another standard texture database. View full abstract»

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  • Maximum-likelihood image estimation using photon-correlated beams

    Page(s): 838 - 846
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB) |  | HTML iconHTML  

    A theory is presented addressing the fundamental limits of image estimation in a setup that uses two photon-correlated beams. These beams have the property that their photon arrivals, as a point process, are ideally synchronized in time and space. The true image represents the spatial distribution of the optical transmittance (or reflectance) of an object. In this setup, one beam is used to probe the image while the other is used as a reference providing additional information on the actual number of photons impinging on the object. This additional information is exploited to reduce the effect of quantum noise associated with the uncertainty in the number of photons per pixel. A stochastic model for the joint statistics of the two observation matrices is developed and used to obtain a local maximum-likelihood estimator of the image. The model captures the nonideal nature of the correlation between the photons of the beams by means of a simple random translation model. The mean-square error of the estimator is evaluated and compared to the corresponding conventional techniques. Conditions for the performance advantage of the proposed estimator are examined in terms of key system parameters. The theoretical predictions are demonstrated by means of simulation. View full abstract»

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  • Information-theoretic matching of two point sets

    Page(s): 868 - 872
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (249 KB)  

    This paper describes the theoretic roadmap of least relative entropy matching of two point sets. The novel feature is to align two point sets without needing to establish explicit point correspondences. The recovery of transformational geometry is achieved using a mixture of principal axes registrations, whose parameters are estimated by minimizing the relative entropy between the two point distributions and using the expectation-maximization algorithm. We give evidence of the optimality of the method and we then evaluate the algorithm's performance in both rigid and nonrigid image registration cases. View full abstract»

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  • A joint estimation approach for two-tone image deblurring by blind deconvolution

    Page(s): 847 - 858
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (515 KB)  

    A new statistical method is proposed for deblurring two-tone images, i.e., images with two unknown grey levels, that are blurred by an unknown linear filter. The key idea of the proposed method is to adjust a deblurring filter until its output becomes two tone. Two optimization criteria are proposed for the adjustment of the deblurring filter. A three-step iterative algorithm (TSIA) is also proposed to minimize the criteria. It is proven mathematically that by minimizing either of the criteria, the original (nonblurred) image, along with the blur filter, will be recovered uniquely (only with possible scale/shift ambiguities) at high SNR. The recovery is guaranteed not only for i.i.d. images but also for correlated and nonstationary images. It does not require a priori knowledge of the statistical parameters or the tone values of the original image; neither does it require a priori knowledge of the phase or other special information (e.g., FIR, symmetry, nonnegativity, etc.) about the blur filter. Numerical experiments are carried out to test the method on synthetic and real images. View full abstract»

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  • Compact encoding of 3-D voxel surfaces based on pattern code representation

    Page(s): 932 - 943
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (437 KB) |  | HTML iconHTML  

    We propose a lossless compression algorithm for three-dimensional (3-D) binary voxel surfaces, based on the pattern code representation (PCR). In PCR, a voxel surface is represented by a series of pattern codes. The pattern of a voxel v is defined as the 3 × 3 × 3 array of voxels, centered on v. Therefore, the pattern code for v informs of the local shape of the voxel surface around v. The proposed algorithm can achieve the coding gain, since the patterns of adjacent voxels are highly correlated to each other. The performance of the proposed algorithm is evaluated using various voxel surfaces, which are scan-converted from triangular mesh models. It is shown that the proposed algorithm requires only 0.5∼1 bits per black voxel (bpbv) to store or transmit the voxel surfaces. View full abstract»

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  • Optimal multidimensional bit-rate control for video communication

    Page(s): 873 - 885
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (439 KB) |  | HTML iconHTML  

    In conventional bit-rate control, the buffer level is controlled by adapting the quantization step size with a fixed frame rate and spatial resolution. We consider a multidimensional (M-D) bit-rate control where the frame rate, spatial resolution and quantization step size are jointly adapted for buffer control. We introduce a fundamental framework to formalize the description of the M-D buffer-constrained allocation problem. Given a set of operating points on a M-D grid to code a nonstationary source in a buffer-constrained environment, we formulate the optimal solution. The formulation allows a skipped frame to be reconstructed from one coded frame using any temporal interpolation method and is shown to be a generalization of formulations considered in the literature. In the case of intraframe coding, a dynamic programming algorithm is introduced to find the optimal solution. The algorithm allows one to compare operational rate-distortion bounds of the M-D and conventional approaches. We also discuss how a solution can be obtained for the case of interframe coding using the optimal dynamic programming algorithm for intraframe coding by making an independent allocation approximation. We illustrate that the M-D approach can provide bit-rate reductions over 50%. We also show that the M-D approach with limited-lookahead provides a slightly suboptimal solution that consistently outperforms the conventional approach with full-lookahead. View full abstract»

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  • Multispectral image visualization through first-order fusion

    Page(s): 923 - 931
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB) |  | HTML iconHTML  

    We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first-order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first-order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed. A variety of experimental results are presented to support the performance of the new method. View full abstract»

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  • Group testing for image compression

    Page(s): 901 - 911
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (321 KB) |  | HTML iconHTML  

    This paper presents group testing for wavelets (GTW), a novel embedded-wavelet-based image compression algorithm based on the concept of group testing. We explain how group testing is a generalization of the zerotree coding technique for wavelet-transformed images. We also show that Golomb coding is equivalent to Hwang's group testing algorithm (Du and Hwang 1993). GTW is similar to SPIHT (Said and Pearlman 1996) but replaces SPIHT's significance pass with a new group testing based method. Although no arithmetic coding is implemented, GTW performs competitively with SPIHT's arithmetic coding variant in terms of rate-distortion performance. View full abstract»

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  • Wavelet-based rotational invariant roughness features for texture classification and segmentation

    Page(s): 825 - 837
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (709 KB) |  | HTML iconHTML  

    We introduce a rotational invariant feature set for texture segmentation and classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single-scale features are combined with multiple-scale features for a more complete textural representation. Wavelets are employed for the computation of single- and multiple-scale roughness features because of their ability to extract information at different resolutions. Features are extracted in multiple directions using directional wavelets, and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information. An iterative K-means scheme is used for segmentation, and a simplified form of a Bayesian classifier is used for classification. The use of the roughness feature set results in high-quality segmentation performance. Furthermore, it is shown that the roughness feature set exhibits a higher classification rate than other feature vectors presented in this work. The feature set retains the important properties of FD-based features, namely insensitivity to absolute illumination and contrast. View full abstract»

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  • Color object indexing and retrieval in digital libraries

    Page(s): 912 - 922
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (367 KB) |  | HTML iconHTML  

    In our previous work, illumination invariant object recognition was achieved by normalizing the three color bands. We further employed the compressed histogram of the chromaticity to arrive at a valuable representation of an object which can facilitate high retrieval accuracy. The first shortcoming of this method lies in the usage of a uniform quantization scheme in obtaining the chromaticity, which is not in agreement with the perception of the human vision system. In this paper, we develop an approach using the CIE UCS transform to circumvent this problem. Second, instead of using uncompressed images to achieve the illumination invariant indexing and retrieval, we carry out our indexing process directly in the DCT domain by using several coefficients from each macro-block. Third, in light of the special properties of the normalized chromaticity histogram frames, the foundation of the ensuing low-pass filtering, an additional step is inserted to render this frame smoother thus resulting in a better data reduction. Fourth, in order to facilitate efficient retrieval during the data query phase, which is of utmost importance in digital libraries, the 36-dimensional model vectors as the indices of model images in digital libraries are clustered by use of vector quantization techniques. This clustering strategy reduces the searching space by an order of magnitude. Desirable results have been observed from our experiments using the proposed color-object-indexing/retrieval algorithm. View full abstract»

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  • New architecture for dynamic frame-skipping transcoder

    Page(s): 886 - 900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1059 KB)  

    Transcoding is a key technique for reducing the bit rate of a previously compressed video signal. A high transcoding ratio may result in an unacceptable picture quality when the full frame rate of the incoming video bitstream is used. Frame skipping is often used as an efficient scheme to allocate more bits to the representative frames, so that an acceptable quality for each frame can be maintained. However, the skipped frame must be decompressed completely, which might act as a reference frame to nonskipped frames for reconstruction. The newly quantized discrete cosine transform (DCT) coefficients of the prediction errors need to be re-computed for the nonskipped frame with reference to the previous nonskipped frame; this can create undesirable complexity as well as introduce re-encoding errors. In this paper, we propose new algorithms and a novel architecture for frame-rate reduction to improve picture quality and to reduce complexity. The proposed architecture is mainly performed on the DCT domain to achieve a transcoder with low complexity. With the direct addition of DCT coefficients and an error compensation feedback loop, re-encoding errors are reduced significantly. Furthermore, we propose a frame-rate control scheme which can dynamically adjust the number of skipped frames according to the incoming motion vectors and re-encoding errors due to transcoding such that the decoded sequence can have a smooth motion as well as better transcoded pictures. Experimental results show that, as compared to the conventional transcoder, the new architecture for frame-skipping transcoder is more robust, produces fewer requantization errors, and has reduced computational complexity. View full abstract»

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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