Texture segmentation using adaptive Gabor filters based on HVS

A texture segmentation algorithm based on HVS (Human Visual System) is proposed in this paper. Psychophysical and Neurophysiological conclusions have supported the hypothesis that the processing of afferent pictorial information in the HVS (the visual cortex in particular) involves two stages: the preattentive stage, and the focused attention stage. To simulate the preattentive stage of HVS, ring and wedge filtering methods are used to segment coarsely and the texture number in the input image is gotten. As texture is the repeating patterns of local variations in image intensity, we can use a part of the texture as the whole region representation. The inscribed squares in the coarse regions are transformed respectively to frequency domain and each spectrum is analyzed in detail. New texture measurements based on the Fourier spectrums are given. Through analyzing the measurements of the texture, including repeatability directionality and regularity, we can extract the feature, and determine the parameters of the Gabor filter-bank. Then to simulate the focused attention stage of HVS, the determined Gabor filter-bank is used to filter the original input image to produce fine segmentation regions. This approach performs better in computational complexity and feature extraction than the fixed parameters and fixed stages Gabor filter-bank approaches.