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  • Title: Reduced complexity rotation invariant texture classification using a blind deconvolution approach.
    Author: Campisi P, Colonnese S, Panci G, Scarano G.
    Journal: IEEE Trans Pattern Anal Mach Intell; 2006 Jan; 28(1):145-9. PubMed ID: 16402627.
    Abstract:
    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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