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