Rotation-invariant texture classification using modified Gabor filters

A method of rotation invariant texture classification based on a joint space-frequency model is introduced. Multiresolution filters, based on a truly analytic form of a polar 2-D Gabor (1946) wavelet, are used to compute spatial frequency-specific but spatially localized microfeatures. These microfeatures constitute an approximate basis set for the representation of the texture sample. The essential characteristics of a texture sample, its macrofeatures, are derived from the statistics of its microfeatures. A texture is modeled as a multivariate Gaussian distribution of macrofeatures. Classification is based on a rotation invariant subset of macrofeatures.

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