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Image Processing, IEEE Transactions on

Issue 9 • Date Sept. 2003

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Displaying Results 1 - 14 of 14
  • Comments on "A translation- and scale-invariant adaptive wavelet transform"

    Page(s): 1091 - 1093
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB) |  | HTML iconHTML  

    H. Xiong et al. (see ibid., vol.9, p.2100-8, 2000) presented a translationand scale-invariant adaptive wavelet transform. We show that their renormalized signal (pre-wavelet transform) is already translation- and scale-invariant. Thus, it is not just invariant under the wavelet transform. We give a necessary and sufficient condition for renormalized signals to be affine transform invariant. View full abstract»

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  • Minimum description length synthetic aperture radar image segmentation

    Page(s): 995 - 1006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1693 KB) |  | HTML iconHTML  

    We present a new minimum description length (MDL) approach based on a deformable partition - a polygonal grid - for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid. This approach then leads to a global MDL criterion without an undetermined parameter since no other regularization term than the stochastic complexity of the polygonal grid is necessary and noise parameters can be estimated with maximum likelihood-like approaches. The performance of this technique is illustrated on synthetic and real synthetic aperture radar images of agricultural regions and the influence of different terms of the model is analyzed. View full abstract»

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  • The hierarchical structure of images

    Page(s): 1067 - 1079
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (857 KB)  

    Using a Gaussian scale space, one can use the extra dimension, viz. scale, for investigation of "built-in" properties of the image in scale space. We show that one of such induced properties is the nesting of special iso-intensity manifolds, which yield an implicitly present hierarchy of the critical points and regions of their influence, in the original image. Its very nature allows one not only to segment the original image automatically, but also to apply "logical filters" to it, obtaining simplified images. We give an algorithm deriving this hierarchy and show its effectiveness on two different kinds of images, both with respect to segmentation and simplification. View full abstract»

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  • Automatic gait recognition based on statistical shape analysis

    Page(s): 1120 - 1131
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (954 KB)  

    Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm. View full abstract»

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  • Synthetic aperture inversion for arbitrary flight paths and nonflat topography

    Page(s): 1035 - 1043
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (989 KB)  

    The paper considers synthetic aperture radar (SAR) and other synthetic aperture imaging systems in which a backscattered wave is measured from positions along an arbitrary (known) flight path. The received backscattered signals are used to produce an image of the terrain. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We focus on cases in which the antenna footprint is so large that the standard narrow-beam algorithms are not useful. We show that certain artifacts can be avoided if the antenna and antenna footprint avoid particular relationships with the ground topography. We give an explicit backprojection imaging algorithm that corrects for the ground topography, flight path, antenna beam pattern, source waveform, and other geometrical factors. For the case of a non-directional antenna, the image produced by the above algorithm contains artifacts. For this case, we analyze the strength of the artifacts relative to the strength of the true image. The analysis shows that the artifacts can be somewhat suppressed by increasing the frequency, integration time, and the curvature of the flight path. View full abstract»

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  • One-dimensional dense disparity estimation for three-dimensional reconstruction

    Page(s): 1107 - 1119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2068 KB) |  | HTML iconHTML  

    We present a method for fully automatic three-dimensional (3D) reconstruction from a pair of weakly calibrated images in order to deal with the modeling of complex rigid scenes. A two-dimensional (2D) triangular mesh model of the scene is calculated using a two-step algorithm mixing sparse matching and dense motion estimation approaches. The 2D mesh is iteratively refined to fit any arbitrary 3D surface. At convergence, each triangular patch corresponds to the projection of a 3D plane. The proposed algorithm relies first on a dense disparity field. The dense field estimation modelized within a robust framework is constrained by the epipolar geometry. The resulting field is then segmented according to homographic models using iterative Delaunay triangulation. In association with a weak calibration and camera motion estimation algorithm, this 2D planar model is used to obtain a VRML-compatible 3D model of the scene. View full abstract»

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  • A VQ-based blind image restoration algorithm

    Page(s): 1044 - 1053
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1725 KB) |  | HTML iconHTML  

    Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms. View full abstract»

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  • On designing an isotropic fiducial mark

    Page(s): 1054 - 1066
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1055 KB)  

    The Crame´r-Rao lower bound (CRLB) of image registration error using an isotropic fiducial mark is derived. The derived CRLB is a function of the intensity profile of the fiducial mark. Following the development of the CRLB, a new method for designing an isotropic fiducial mark, suitable for digital image registration, is presented. A parameterization method of the fiducial intensity profile is introduced which guarantees no aliasing effect when the fiducial mark is digitized with the proper sampling rate. A method for computing the fiducial intensity profile, based on minimization of the CRLB registration error, and subject to certain practical constraints, is developed. For imaging systems with a significant low-pass effect, it is proposed to pre-emphasize the high frequency components of the fiducial mark by converting the designed gray-scale fiducial marks into binary fiducial marks. Experimental results show that the designed fiducial mark can provide very accurate registration results and that the registration accuracy is independent of its location. View full abstract»

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  • L/M-fold image resizing in block-DCT domain using symmetric convolution

    Page(s): 1016 - 1034
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4059 KB)  

    Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine transform (DCT) domain, which exploits the multiplication-convolution property of DCT (multiplication in the spatial domain corresponds to symmetric convolution in the DCT domain). When an image is given in terms of its 8×8 block-DCT coefficients, its resized image is also obtained in 8×8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR. View full abstract»

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  • Multichannel blind iterative image restoration

    Page(s): 1094 - 1106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1575 KB)  

    Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun. View full abstract»

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  • Symmetric region growing

    Page(s): 1007 - 1015
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (687 KB) |  | HTML iconHTML  

    Of the many proposed image segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as symmetric region growing algorithms, leads to a single-pass region-growing algorithm applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Results illustrate the method's efficiency and its application to 3D medical image segmentation. View full abstract»

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  • Method of paired transforms for reconstruction of images from projections: discrete model

    Page(s): 985 - 994
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1082 KB)  

    An effective method of discrete image reconstruction from its projections is introduced. The method is based on the vector and paired representations of the two-dimensional (2D) image with respect to the 2D discrete Fourier transform. Such representations yield algorithms for image reconstruction by a minimal number of attenuation measurements in certain projections. The proposed algorithms are described in detail for an N×N image, when N=2r, r>1. The inverse formulas for image reconstruction are given. The efficiency of the algorithms is expressed in the fact that they require a minimal number of multiplications, or can be implemented without such at all. The problem of discrete image reconstruction is also considered in three-dimensional (3D) space, namely on the 3D torus, where the reconstruction is performed by means of the nonlinear projections that are integral over 3D spirals on the torus. View full abstract»

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  • Mathematical properties of the JPEG2000 wavelet filters

    Page(s): 1080 - 1090
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (638 KB) |  | HTML iconHTML  

    The LeGall 5/3 and Daubechies 9/7 filters have risen to special prominence because they were selected for inclusion in the JPEG2000 standard. We determine their key mathematical features: Riesz bounds, order of approximation, and regularity (Holder and Sobolev). We give approximation theoretic quantities such as the asymptotic constant for the L2 error and the angle between the analysis and synthesis spaces which characterizes the loss of performance with respect to an orthogonal projection. We also derive new asymptotic error formulae that exhibit bound constants that are proportional to the magnitude of the first nonvanishing moment of the wavelet. The Daubechies 9/7 stands out because it is very close to orthonormal, but this turns out to be slightly detrimental to its asymptotic performance when compared to other wavelets with four vanishing moments. View full abstract»

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  • Down-scaling for better transform compression

    Page(s): 1132 - 1144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1257 KB) |  | HTML iconHTML  

    The most popular lossy image compression method used on the Internet is the JPEG standard. JPEG's good compression performance and low computational and memory complexity make it an attractive method for natural image compression. Nevertheless, as we go to low bit rates that imply lower quality, JPEG introduces disturbing artifacts. It is known that, at low bit rates, a down-sampled image, when JPEG compressed, visually beats the high resolution image compressed via JPEG to be represented by the same number of bits. Motivated by this idea, we show how down-sampling an image to a low resolution, then using JPEG at the lower resolution, and subsequently interpolating the result to the original resolution can improve the overall PSNR performance of the compression process. We give an analytical model and a numerical analysis of the down-sampling, compression and up-sampling process, that makes explicit the possible quality/compression trade-offs. We show that the image auto-correlation can provide a good estimate for establishing the down-sampling factor that achieves optimal performance. Given a specific budget of bits, we determine the down-sampling factor necessary to get the best possible recovered image in terms of PSNR. 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