1 May 2003 Bayes factors for edge detection from wavelet product spaces
Author Affiliations +
Optical Engineering, 42(5), (2003). doi:10.1117/1.1564104
Abstract
Interband wavelet correlation provides one approach to defining edges in an image. Interband wavelet products follow long-tailed density distributions, and in such a context thresholding is very difficult. We show how segmentation using a Markov-field spatial dependence model is a more appropriate approach to demarcating edge and nonedge regions. A key part of this work is quantitative assessment of goodness of edge versus nonedge fit. We introduce a formal assessment framework based on Bayes factors. A detailed example is used to illustrate these results.
Fionn D. Murtagh, Jean-Luc Starck, "Bayes factors for edge detection from wavelet product spaces," Optical Engineering 42(5), (1 May 2003). http://dx.doi.org/10.1117/1.1564104
JOURNAL ARTICLE
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KEYWORDS
Wavelets

Data modeling

Expectation maximization algorithms

Image segmentation

Wavelet transforms

Edge detection

Model-based design

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