Accurate set-up of Gabor filters for texture classification

Gabor filters are of particular interest to the computer vision community because the profiles of the two dimensional Gabor functions have been shown to closely approximate the receptive field profiles of particular simple cells in the visual cortex of certain mammals. However, only a few values for the parameters of the Gabor function can generate a large number of filters that makes practical implementation impossible. Moreover, the process of adjusting the parameters of these functions to obtain the 'best' set is not straightforward. In this paper we describe a new reliable and systematic method for setting up Gabor filters for texture classification. Texture is an intrinsic property of images and is thus an important feature for computer vision. Gabor filters are used to extract the features from local neighborhoods of the texture images and have been tuned for the classification of initially; naturally occurring textures from Brodatz's album and then different grades of Ceramic filters used in molten metal filtration.