1 May 2003 Bayes factors for edge detection from wavelet product spaces
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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). https://doi.org/10.1117/1.1564104

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