Reduced complexity rotation invariant texture classification using a blind deconvolution approach
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-dimens...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 28(2006), 1 vom: 24. Jan., Seite 145-9 |
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| Weitere Verfasser: | , , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2006
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Evaluation Study Journal Article |
| Zusammenfassung: | 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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| Beschreibung: | Date Completed 01.02.2006 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1939-3539 |