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.

CAMPISI P, S.COLONNESE, G.PANCI, & G. SCARANO (2006). Reduced complexity rotation-invariant texture classification using a blind deconvolution approach. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 28, 145-149 [10.1109/TPAMI.2006.24].

Reduced complexity rotation-invariant texture classification using a blind deconvolution approach

CAMPISI, PATRIZIO;
2006

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/142555
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