Bayes factors for edge detection from wavelet product spaces

Fionn D. Murtagh; Jean-Luc Starck

doi:10.1117/1.1564104

1 May 2003 Bayes factors for edge detection from wavelet product spaces

Fionn D. Murtagh, Jean-Luc Starck

Optical Engineering, Vol. 42, Issue 5, (May 2003). https://doi.org/10.1117/1.1564104

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.

©(2003) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Fionn D. Murtagh and 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

Published: 1 May 2003

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