DocumentCode :
2968862
Title :
A deterministic iterative algorithm for HMRF-textured image segmentation
Author :
Shirazi, Mehdi N. ; Noda, Hideki
Author_Institution :
Dept. of Electr. Eng., Kyoto Univ., Japan
Volume :
3
fYear :
1993
fDate :
25-29 Oct. 1993
Firstpage :
2189
Abstract :
The problem of textured image segmentation is considered. A textured image is modeled by a hierarchical Markov random field (HMRF). The image segmentation is realized as the maximum a posteriori (MAP) estimate of the textured regions. Following an argument based on the mean field approximation, a deterministic iterative algorithm is proposed which searches for the MAP segmentation of the textured image.
Keywords :
Markov processes; estimation theory; image segmentation; image texture; iterative methods; optimisation; convex optimisation; deterministic iterative algorithm; estimation theory; hierarchical Markov random; mean field approximation; textured image segmentation; Distribution functions; Geometry; Image restoration; Image segmentation; Iterative algorithms; Lattices; Markov random fields; Random variables; State-space methods; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
Type :
conf
DOI :
10.1109/IJCNN.1993.714160
Filename :
714160
Link To Document :
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