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Image segmentation by fuzzy rule and Kohonen-constraint satisfaction fuzzy c-mean
Author(s)
Khunkay, Suraphon
Paithoonwattanakij, Kitti
Date Issued
December 1, 1997
Type
Conference Paper
Abstract
In this paper, we present a new algorithm that can segment a fuzzy data. This method is based on fuzzy logic and neural network, and a concept of constraint satisfaction problem (CSP). Firstly, pre-processing by creating a new pixel value on fuzzy rule has ability to describe the effect of neighborhood pixel in degree of membership value or linguistic variables which are utilized to activate a rule base. Secondly, the feature extraction employs Kohonen feature mapping (SOM), which can learn a feature without prior knowledge. Finally, a fuzzy c-mean (FCM) adapts to CSP structure by constituting a interconnection weight for all membership value of fuzzy c-mean with a global consistency situation. The result of this method have been tested on a varieties images.
Citation
Proceedings of the International Conference on Information Communications and Signal Processing Icics, 2, 713-717, 1997