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Rotation-invariant texture classification using a complete space-frequency model

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2 Author(s)
Haley, G.M. ; Ameritech Services, Hoffman Estates, IL, USA ; Manjunath, B.S.

A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving microfeatures. From these microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets

Published in:

Image Processing, IEEE Transactions on  (Volume:8 ,  Issue: 2 )

Date of Publication:

Feb 1999

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