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Proceedings Paper

Adaptive Bayesian approach for color image segmentation
Author(s): Michael M. Chang; Andrew J. Patti; M. Ibrahim Sezan; A. Murat Tekalp
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Paper Abstract

A Bayesian segmentation algorithm to separate color images into regions of distinct colors is presented. The algorithm takes into account the local color variations in the image in an adaptive manner. A Gibbs random field (GRF) is used as the a priori probability model for the segmentation process to impose a spatial connectivity constraint. We study the performance of the proposed algorithm in different color spaces and its application in reduced data rendering of color images. Experimental results and discussion are included.

Paper Details

Date Published: 22 October 1993
PDF: 10 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157998
Show Author Affiliations
Michael M. Chang, Univ. of Rochester (United States)
Andrew J. Patti, Univ. of Rochester (United States)
M. Ibrahim Sezan, Eastman Kodak Co. (United States)
A. Murat Tekalp, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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