DocumentCode :
2876851
Title :
A new image segmentation algorithm based the fusion of Markov random field and fuzzy c-means clustering
Author :
Liu, Siyuan ; Li, Xiaofeng ; Li, Zaiming
Author_Institution :
Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Volume :
1
fYear :
2005
fDate :
12-14 Oct. 2005
Firstpage :
144
Lastpage :
147
Abstract :
A new image segmentation algorithm based on the fusion of Markov random field and fuzzy c-means clustering (FCM) is proposed in this paper. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noises. For improving the robustness of FCM to noises, we use Markov random field model to represent the spatial constraint information of an image and based on the fusion of Markov spatial constraint field and the fuzzy segmentation information resulting from FCM, the new algorithm overcomes the problem of FCM and keeps the computation simplicity. The results of experiments prove the robustness and validity our algorithm.
Keywords :
Markov processes; fuzzy set theory; image segmentation; pattern clustering; sensor fusion; Markov random field; fuzzy c-means clustering; image segmentation algorithm; spatial constraint information; Clustering algorithms; Image segmentation; Markov random fields; Noise robustness; Noise shaping; Pixel; Random variables; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications and Information Technology, 2005. ISCIT 2005. IEEE International Symposium on
Print_ISBN :
0-7803-9538-7
Type :
conf
DOI :
10.1109/ISCIT.2005.1566818
Filename :
1566818
Link To Document :
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