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.
Titolo: | Reduced complexity rotation-invariant texture classification using a blind deconvolution approach |
Autori: | |
Data di pubblicazione: | 2006 |
Rivista: | |
Citazione: | 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. |
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. |
Handle: | http://hdl.handle.net/11590/142555 |
Appare nelle tipologie: | 1.1 Articolo in rivista |