A Bayesian Segmentation Methodology for Parametric Image Models

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A Bayesian Segmentation Methodology for Parametric Image Models (1993)

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by Steven M. Lavalle , Seth A. Hutchinson

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	27 - 5 self

BibTeX

@ARTICLE{Lavalle93abayesian,
    author = {Steven M. Lavalle and Seth A. Hutchinson},
    title = {A Bayesian Segmentation Methodology for Parametric Image Models},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {1993},
    volume = {17},
    pages = {211--217}
}

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Abstract

Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. Our approach does not require parameter estimation, and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. We apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. We present results on a variety of real range and intensity images. 1 Introduction The problem of image segmentation, partitioning an image into a set of homogeneous regions, is a fundamental problem in computer vision. Approaches to the segmentation problem can be grouped...

Keyphrases

bayesian segmentation methodology parametric image model image segmentation parameter estimation intensity image novel approach distinct parametric model family implicit polynomial surface gaussian markov random field estimation-based method parameter estimate computer vision region-based image segmentation method general statistical context segmentation problem real range fundamental problem past segmentation scheme little information present result parametric polynomial surface homogeneous region bayesian probability

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