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IEEE Transactions on Information Theory

Issue 5 • Aug. 2000

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Displaying Results 1 - 19 of 19
  • Introduction to the special issue on information-theoretic imaging

    Publication Year: 2000, Page(s):1709 - 1713
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    Freely Available from IEEE
  • An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

    Publication Year: 2000, Page(s):1927 - 1932
    Cited by:  Papers (180)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (320 KB)

    A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity and discrimination... View full abstract»

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  • Higher order (nonlinear) diffraction tomography: inversion of the Rytov series

    Publication Year: 2000, Page(s):1748 - 1761
    Cited by:  Papers (24)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (284 KB)

    Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the complex phase of the wavefields is modeled by an arbitrarily large (possibly infinite) number of terms in the Rytov series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of diffraction tom... View full abstract»

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  • Limits to estimation in stochastic ill-conditioned inverse problems

    Publication Year: 2000, Page(s):1872 - 1880
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (224 KB)

    Using information-theoretic methods we develop simple results quantifying a lower bound for minimax estimation, a kind of infinite-dimensional Cramer-Rao lower bound, for signal estimation in possibly nonlinear, ill-conditioned, inverse problems. Our results reduce calculation to a geometric computation based on a modulus of continuity and make explicit connections with results in the literature o... View full abstract»

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  • Rate-distortion theory applied to automatic object recognition

    Publication Year: 2000, Page(s):1921 - 1927
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (292 KB)

    We consider the problem of recognizing CAD models at arbitrary orientations observed via the projective transformation on an imaging sensor with noise. Bounds on codebook size are established through the rate-distortion curve for a distortion measure derived from the Hilbert-Schmidt norm for elements of the orthogonal group View full abstract»

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  • Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models

    Publication Year: 2000, Page(s):1826 - 1841
    Cited by:  Papers (59)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1032 KB)

    This paper treats a multiresolution hidden Markov model for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the expectation-maximization algorithm. An image is classifie... View full abstract»

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  • Radio-astronomical imaging in the presence of strong radio interference

    Publication Year: 2000, Page(s):1730 - 1747
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1020 KB)

    Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio-astronomical imaging. Current deconvolution procedures, such as CLEAN, are shown to be unsuitable for spatially filtered data, and the ... View full abstract»

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  • Hyperspectral imagery: clutter adaptation in anomaly detection

    Publication Year: 2000, Page(s):1855 - 1871
    Cited by:  Papers (57)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (388 KB)

    Hyperspectral sensors are passive sensors that simultaneously record images for hundreds of contiguous and narrowly spaced regions of the electromagnetic spectrum. Each image corresponds to the same ground scene, thus creating a cube of images that contain both spatial and spectral information about the objects and backgrounds in the scene. In this paper, we present an adaptive anomaly detector de... View full abstract»

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  • Estimating the orientation of planar surfaces: algorithms and bounds

    Publication Year: 2000, Page(s):1908 - 1920
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    This paper presents a computationally and statistically efficient parametric solution to the problem of estimating the orientation in space of a planar textured surface from a single, noisy, observed image of it. The coordinate transformation from surface to image coordinates, due to the perspective projection, transforms each homogeneous sinusoidal component of the surface texture into a sinusoid... View full abstract»

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  • A statistical multiscale framework for Poisson inverse problems

    Publication Year: 2000, Page(s):1811 - 1825
    Cited by:  Papers (72)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (564 KB)

    This paper describes a statistical multiscale modeling and analysis framework for linear inverse problems involving Poisson data. The framework itself is founded upon a multiscale analysis associated with recursive partitioning of the underlying intensity, a corresponding multiscale factorization of the likelihood (induced by this analysis), and a choice of prior probability distribution made to m... View full abstract»

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  • Generalized matched filters and univariate Neyman-Pearson detectors for image target detection

    Publication Year: 2000, Page(s):1932 - 1937
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (496 KB)

    I derive two-stage, statistically suboptimal target detectors for images. The first, or transformation, stage is a “generalized matched filter” (GMF) that linearly transforms the input image. I propose three rational signal-to-noise-ratio criteria whose maximization yields the three GMFs. The second, or detection, stage is a univariate “Neyman-Pearson detector” (NPD), which... View full abstract»

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  • Statistical imaging and complexity regularization

    Publication Year: 2000, Page(s):1762 - 1777
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (780 KB)

    We apply the complexity regularization principle to statistical ill-posed inverse problems in imaging. The class of problems studied includes restoration of images corrupted by Gaussian or Poisson noise and nonlinear transformations. We formulate a natural distortion measure in image space and develop nonasymptotic bounds on estimation performance in terms of an index of resolvability that charact... View full abstract»

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  • Asymptotic global confidence regions in parametric shape estimation problems

    Publication Year: 2000, Page(s):1881 - 1895
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    We introduce confidence region techniques for analyzing and visualizing the performance of two-dimensional parametric shape estimators. Assuming an asymptotically normal and efficient estimator for a finite parameterization of the object boundary, Cramer-Rao bounds are used to define an asymptotic confidence region, centered around the true boundary. Computation of the probability that an entire b... View full abstract»

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  • Ab initio reconstruction and experimental design for cryo electron microscopy

    Publication Year: 2000, Page(s):1714 - 1729
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3120 KB)

    A statistical model for the object and the complete image formation process in the cryo electron microscopy of viruses is presented. Using this model, maximum-likelihood reconstructions of the three-dimensional (3-D) structure of viruses are computed using the expectation maximization algorithm, and alternative experimental designs are evaluated based on Cramer-Rao bounds. Numerical examples of th... View full abstract»

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  • Model-based classification of radar images

    Publication Year: 2000, Page(s):1842 - 1854
    Cited by:  Papers (38)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3008 KB)

    A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Ba... View full abstract»

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  • Wavelet thresholding via MDL for natural images

    Publication Year: 2000, Page(s):1778 - 1788
    Cited by:  Papers (54)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (232 KB)

    We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achie... View full abstract»

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  • Information measures for object recognition accommodating signature variability

    Publication Year: 2000, Page(s):1896 - 1907
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (728 KB)

    This paper presents measures characterizing the information content of remote observations of ground scenes imaged via optical and infrared sensors. Object recognition is posed in the context of deformable templates; the special Euclidean group is used to accommodate geometric variation of object pose. Principal component analysis of object signatures is used to represent and efficiently accommoda... View full abstract»

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  • Tiling and adaptive image compression

    Publication Year: 2000, Page(s):1789 - 1799
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (236 KB)

    We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In this task, using a larger number of regions would result in better compression, but would also require more bits for describing the regions and the probability models used in the regions. We discuss using quadtree methods for performing the compression. We introd... View full abstract»

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  • Kullback proximal algorithms for maximum-likelihood estimation

    Publication Year: 2000, Page(s):1800 - 1810
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (940 KB)

    Accelerated algorithms for maximum-likelihood image reconstruction are essential for emerging applications such as three-dimensional (3-D) tomography, dynamic tomographic imaging, and other high-dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum-likelihood estimates and study its convergence propert... View full abstract»

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Aims & Scope

IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Prakash Narayan 

Department of Electrical and Computer Engineering