Texture Segmentation Images using Gabor Filters
In this project, texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system.In this 40 different Gabor filters are applied on an image will result 40 different images with different orientations. This paper addresses a novel algorithm in order to detect text features and extract their corresponding geometric points The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. We propose a systematic filter selection scheme which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. In this paper we employ this multichannel approch to the problem in order to gain insight into the ability of this methodology in solving texture segmentation problem.
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