Integrated approach to texture segmentation using multiple Gabor filters

This paper presents an integrated approach using multiple Gabor filters for the segmentation of multi-textured images. The approach includes both the design of the constituent Gabor filters and the design of the classifier and postprocessing. The classifier uses a mixture density to reduce localization error at texture boundaries, and the postprocessing uses morphological operators to remove spurious misclassifications at texture boundaries. Results are presented that confirm the efficacy of the postprocessing methods and the overall integrated approach.

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