Quantization from Bayes factors with application to multilevel thresholding

F. Murtagh; J. L. Starck

doi:10.1016/S0167-8655(03)00038-2

Quantization from Bayes factors with application to multilevel thresholding

F. Murtagh, J. L. Starck

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

We are concerned with the optimal selection of multiple thresholds in image analysis. We propose the use of the Bayes information criterion, a minimal information measure, for this and illustrate its use in practical cases. Applications of multiple threshold selection of interest to us include the closely related problems of (i) quantization for lossy encoding, and (ii) segmentation. Our examples relate to segmentation as a post-processing phase in edge detection.

Original language	English
Pages (from-to)	2001-2007
Number of pages	7
Journal	Pattern Recognition Letters
Volume	24
Issue number	12
Early online date	25 Mar 2003
DOIs	https://doi.org/10.1016/S0167-8655(03)00038-2
Publication status	Published - 1 Aug 2003
Externally published	Yes

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title = "Quantization from Bayes factors with application to multilevel thresholding",

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AB - We are concerned with the optimal selection of multiple thresholds in image analysis. We propose the use of the Bayes information criterion, a minimal information measure, for this and illustrate its use in practical cases. Applications of multiple threshold selection of interest to us include the closely related problems of (i) quantization for lossy encoding, and (ii) segmentation. Our examples relate to segmentation as a post-processing phase in edge detection.

KW - Bayes factor

KW - Bayes information criterion

KW - Edge detection

KW - Image thresholding

KW - Model selection

KW - Wavelet transform

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SP - 2001

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

IS - 12

ER -

Quantization from Bayes factors with application to multilevel thresholding

Abstract

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