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Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach
January 2006 (vol. 28 no. 1)
pp. 145-149
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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Index Terms:
Index Terms- Statistical texture model, texture analysis, texture classification, feature moments.
Citation:
Patrizio Campisi, Stefania Colonnese, Gianpiero Panci, Gaetano Scarano, "Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 145-149, Jan. 2006, doi:10.1109/TPAMI.2006.24
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