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Issue No.01 - January (2006 vol.28)
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.
Index Terms- Statistical texture model, texture analysis, texture classification, feature moments.
Stefania Colonnese, Gianpiero Panci, Patrizio Campisi, "Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.28, no. 1, pp. 145-149, January 2006, doi:10.1109/TPAMI.2006.24
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