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Reduced complexity rotation invariant texture classification using a blind deconvolution approach

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4 Author(s)
P. Campisi ; Dipartimento Elettronica Applicata, Universita degli Roma Tre, Rome, Italy ; S. Colonnese ; G. Panci ; G. Scarano

In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:28 ,  Issue: 1 )