DocumentCode :
2367284
Title :
Segmenting non stationary images with triplet Markov fields
Author :
Benboudjema, Dalila ; Pieczynski, Wojciech
Author_Institution :
Departement CITI, CNRS UMR, Evry, France
Volume :
1
fYear :
2005
fDate :
11-14 Sept. 2005
Abstract :
The hidden Markov field (HMF) model has been used in many model-based solutions to image analysis problems, including that of image segmentation, and generally gives satisfying results. However, when the class image is non stationary, the unsupervised segmentation results provided by HMF can be poor. In this paper, we tackle the problem of modeling a non stationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, to segment non stationary images. Experiments indicate that the new algorithm performs better than the classical one.
Keywords :
Markov processes; image segmentation; hidden Markov field model; image analysis problems; nonstationary images segmentation; triplet Markov fields; unsupervised statistical image segmentation; Bayesian methods; Bibliographies; Electronic mail; Hidden Markov models; Image analysis; Image segmentation; Machine vision; Parameter estimation; Pixel; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2005. ICIP 2005. IEEE International Conference on
Print_ISBN :
0-7803-9134-9
Type :
conf
DOI :
10.1109/ICIP.2005.1529751
Filename :
1529751
Link To Document :
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