A Markov random field approach to data fusion and colour segmentation

Wright, W.A.

doi:10.1016/0262-8856(89)90009-7

Cited by 37 publications

(14 citation statements)

References 9 publications

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“…These were taken from the Universal Symbols (Vol. 17) It can be seen from Figs. 11d, 12d, 13d, 14d, and 15d of the Image Club Graphics Inc.…”

mentioning

confidence: 86%

“…GRFs have been used to model segmentation fields to facilitate formation of spatially continuous regions. Among early work in color segmentation using GRF is Wright [17] who utilized line processes. More recently, Chang et al [18] have extended the adaptive Bayesian segmentation approach of Pappas [19] using a space-varying image intensity model to color images.…”

Section: Introductionmentioning

confidence: 99%

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Region-Based Shape Matching for Automatic Image Annotation and Query-by-Example

Saber

Tekalp

1997

Journal of Visual Communication and Image Representation

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“…These were taken from the Universal Symbols (Vol. 17) It can be seen from Figs. 11d, 12d, 13d, 14d, and 15d of the Image Club Graphics Inc.…”

mentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Region-Based Shape Matching for Automatic Image Annotation and Query-by-Example

Saber

Tekalp

1997

Journal of Visual Communication and Image Representation

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“…Some Over the past two decades, a wide variety of pixel-level image fusion algorithms has been developed. These techniques may be classified into linear superposition, logical filter [17], mathematical morphology [18], image algebra [19] [20], artificial neural network [21], and simulated annealing [22] methods. Each of these algorithms focuses on the fact that the fused image reveals new information concerning features that can not be perceived in individual sensor images.…”

Section: Proof Of Proposition 54mentioning

confidence: 99%

Multiscale Signal Processing and Shape Analysis for an Inverse Sar Imaging System

He¹

2001

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The great challenge in signal processing is to devise computationally efficient and statistically optimal algorithms for estimating signals from noisy background and understanding their contents. This thesis treats the problem of multiscale signal processing and shape analysis for an Inverse Synthetic Aperture Radar (ISAR) imaging system. To address some of the limitations of conventional techniques in radar image processing, an information theoretic approach for target motion estimation is first proposed. A wavelet based multiscale method for shape enhancement is subsequently derived and followed by a regression technique for shape recognition.Building on entropy-based divergence measures which have shown promising results in many areas of engineering and image processing, we introduce in this thesis a new generalized divergence measure, namely the Jensen-Rényi divergence. Upon establishing its properties such as convexity and its upper bound etc., we apply it to image registration for ISAR focusing as well as related problems in data fusion.Attempting to extend current approaches to signal estimation in a wavelet framework, which have generally relied on the assumption of normally distributed perturbations, we propose a novel non-linear filtering technique, as a pre-processing step for the shapes obtained from an ISAR imaging system. The key idea is to project a noisy shape onto a wavelet domain and to suppress wavelet coefficients by a mask derived from curvature extrema in its scale space representation. For a piecewise smooth signal, it can be shown that filtering by this curvature mask is equivalent to preserving the signal pointwise Hölder exponents at the singular points, and to lifting its smoothness at all the remaining points.To identify a shape independently of its registration information, we propose matching two configurations by regression, using notations of general shape spaces and procrustean distances. In particular, we study the generalized matching by estimating mean shapes in two dimensions. Simulation results show that matching by way of a mean shape is more robust than matching target shapes directly. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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“…While other researchers (Hurlbert 1989;Wright 1989) have used the Markov random field formulation in color segmentation, the research of Daily (1989) comes closest in spirit to this work in the selection of hue as a useful measure for image segmentation.…”

Section: Markov Random Field Formulationmentioning

confidence: 99%

“…Interesting attempts to combine information across these spaces (besides intensity) have been made, such as Wright's study of fusing R, G, and B images using Markov random fields (Wright 1989). Still, each of the components in the RGB space are highly correlated and not independent of each other.…”

Section: Rgb Spacementioning

confidence: 99%

Toward color image segmentation in analog VLSI: Algorithm and hardware

Perez

Koch

1994

Int J Comput Vision

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Standard techniques for segmenting color images are based on finding normalized RGB discontinuities, color histogramming, or clustering techniques in RGB or CIE color spaces. The use of the psychophysical variable hue in HSI space has not been popular due to its numerical instability at low saturations. In this article, we propose the use of a simplified hue description suitable for implementation in analog VLSI. We demonstrate that if the integrated white condition holds, hue is invariant to certain types of highlights, shading, and shadows. This is due to the additive/shift invariance property, a property that other color variables lack. The more restrictive uniformly varying lighting model associated with the multiplicative/scale invariance property shared by both hue and normalized RGB allows invariance to transparencies, and to simple models of shading and shadows. Using binary hue discontinuities in conjunction with first-order type of surface interpolation, we demonstrate these invariant properties and compare them against the performance of RGB, normalized RGB, and CIE color spaces. We argue that working in HSI space offers an effective method for segmenting scenes in the presence of confounding cues due to shading, transparency, highlights, and shadows. Based on this work, we designed and fabricated for the first time an analog CMOS VLSI circuit with on-board phototransistor input that computes normalized color and hue.

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A Markov random field approach to data fusion and colour segmentation

Cited by 37 publications

References 9 publications

Region-Based Shape Matching for Automatic Image Annotation and Query-by-Example

Region-Based Shape Matching for Automatic Image Annotation and Query-by-Example

Multiscale Signal Processing and Shape Analysis for an Inverse Sar Imaging System

Toward color image segmentation in analog VLSI: Algorithm and hardware

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