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Reduced complexity rotation invariant texture classification using a blind deconvolution approach

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

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