Automated Texture Analysis with Gabor filter

The texture is very important cue in region based segmentation of images. Texture features play a very important role in computer vision and pattern recognition. Texture Applications include industrial inspection, estimation of object range and orientation, shape analysis, satellite imaging, and medical diagnosis. In this paper, we study of different definitions of texture. The timefrequency transformed based method of texture discrimination, which is in turn based on Gabor filters is done. In Gabor transform, a signal can be represented in terms of sinusoids that are modulated by translated Gaussian windows. The resulting timefrequency decomposition is a suite of local Fourier transforms which displays any nonstationary spectral trends. Here the interrelationship between individual transforms is well-understood and studied. The difficulty in parameter selection for Gabor filter design due to nonorthogonal characteristic of Gabor filters is overcome by real coded genetic algorithm based method.