Applying Real Coded Genetic Algorithms to Gabor Filter Bank Design for Supervised Texture Classification and Segmentation
نویسندگان
چکیده
Supervised classification and segmentation of textured images find immense importance in image analysis applications and still continues to be an open research issue to the Computer Vision community. The high performance texture analysis capability of the human visual system have encouraged the researchers to model the characteristics of the receptive cells of the human eye suggesting a close resemblance to the functioning of Gabor Filter Bank for spatial frequency analysis. In this work, we focus on designing Gabor filter banks using real coded genetic algorithms with the objective of maximum discrimination of texture features.
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