A multistage algorithm which makes use of spatial contextual information in a hierarchical clustering procedure has been developed for unsupervised image segmentation. A Markov random field model is employed to enforce local spatial smoothness, while the maximum entropy principle is utilized to quantify global smoothness in the image processing. A multiwindow approach implemented in a pyramid-like data structure which uses a boundary blocking operation is employed to increase computational efficiency.<>
Unsupervised multistage segmentation using Markov random field and maximum entropy principle
Proceedings of 1st International Conference on Image Processing ; 2 ; 192-196 vol.2
1994-01-01
598488 byte
Conference paper
Electronic Resource
English
Unsupervised Multistage Segmentation using Markov Random Field and Maximum Entropy Principle
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