Benvenuti nell'Anagrafe della Ricerca d'Ateneo
La valutazione delle attività di ricerca che si svolgono nelle università ha assunto un'importanza rilevante sia per l'intero sistema universitario sia all'interno di ogni singolo ateneo. Roma Tre si è quindi dotata di idonei strumenti informativi in grado di supportare al meglio le azioni di monitoraggio e di valutazione delle attività di ricerca svolte nei propri dipartimentied ha realizzato una Anagrafe della Ricerca di Ateneo.
EnglishIn 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 |
File in questo prodotto:
http://hdl.handle.net/11590/142555
Copyright © 2021