The Design of Multiple Gabor Filters for Segmenting Multiple Textures

Gabor filters have been successfully employed in texture segmentation problems, yet a general multi-filter multi-texture Gabor filter design procedure has not been offered. To this end, we first present a multichannel paradigm that provides a mathematical framework for the design of the filters. The paradigm establishes relationships between the predicted texture-segmentation error, the power spectrum of the textures, the parameters of the Gabor filters, the parameters of subsequent Gaussian postfilters, and the predicted vector output statistics of multiple filter channels. Using these mathematical relationships, we develop a Gabor filter design procedure based on selecting the set of filters associated with the lowest predicted texture-segmentation error. We also include a classifier design and postprocessing methods to provide a complete texture-segmentation system. The development of our filter-design procedure and underlying mathematical models provide new insight into the design of multiple Gabor filters for the segmentation of multiple textures. Finally, we present experimental results that confirm the efficacy of our new Gabor-filter design procedure and support the underlying mathematical framework. Keywords—Gabor prefilter, texture segmentation, statistical image analysis, texture analysis, computer vision, image segmentation. EDICS CATEGORIES: IP 1.2, 1.5, 1.6