Robust rotation-invariant texture classification using a model based approach
In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We sho...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 13(2004), 6 vom: 23. Juni, Seite 782-91 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Evaluation Study Journal Article Validation Study |
| Zusammenfassung: | In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space |
|---|---|
| Beschreibung: | Date Completed 10.02.2005 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1941-0042 |