The Community for Technology Leaders
Green Image
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.

G. Scarano, S. Colonnese, G. Panci and P. Campisi, "Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 145-149, 2006.
93 ms
(Ver 3.3 (11022016))