DocumentCode
3060667
Title
The use of Gibbs random fields for image segmentation
Author
Wang, Tao ; Xinhua Zhuang ; Xing, Xiaoliang
Author_Institution
Dept. of Comput. Sci. & Eng., Zhejiang Univ., Hangzhou, China
fYear
1992
fDate
30 Aug-3 Sep 1992
Firstpage
57
Lastpage
60
Abstract
Presents a robust and adaptive technique for segmentation of a noisy image. The original image is modeled by an underlying Gibbs random field, and the noise is the mixture of an additive independent Gaussian noise and a salt or pepper noise. The processes of maximum a posteriori segmentation and maximum-likelihood estimation for the image model parameters are carried out simultaneously
Keywords
image segmentation; maximum likelihood estimation; noise; parameter estimation; Gibbs random fields; additive independent Gaussian noise; image segmentation; maximum a posteriori segmentation; maximum-likelihood estimation; noisy image; parameter estimation; salt or pepper noise; Additive noise; Computer science; Focusing; Gaussian noise; Image segmentation; Machine vision; Maximum likelihood estimation; Noise robustness; Parameter estimation; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location
The Hague
Print_ISBN
0-8186-2920-7
Type
conf
DOI
10.1109/ICPR.1992.201927
Filename
201927
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