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<p>A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures.</p>
pattern recognition; multichannel texture analysis; localized spatial filters; visible textures; irradiance patterns; encoding; region code; region information; two-dimensional Gabor functions; tunable orientation; radial frequency bandwidths; tunable center frequencies; joint resolution; channel amplitude responses; discontinuities; encoding; filtering and prediction theory; pattern recognition

W. Geisler, M. Clark and A. Bovik, "Multichannel Texture Analysis Using Localized Spatial Filters," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 55-73, 1990.
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