Texture analysis using generalized co-occurrence matrices

We present a new approach to texture analysis based on the spatial distribution of local features in unsegmented textures. The textures are described using features derived from generalized co-occurrence matrices (GCM). A GCM is determined by a spatial constraint predicate F and a set of local featu...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 1(1979), 3 vom: 01. März, Seite 251-9
1. Verfasser: Davis, L S (VerfasserIn)
Weitere Verfasser: Johns, S A, Aggarwal, J K
Format: Aufsatz
Sprache:English
Veröffentlicht: 1979
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:We present a new approach to texture analysis based on the spatial distribution of local features in unsegmented textures. The textures are described using features derived from generalized co-occurrence matrices (GCM). A GCM is determined by a spatial constraint predicate F and a set of local features P = {(Xi, Yi, di), i = 1,..., m} where (Xi, Yi) is the location of the ith feature, and di is a description of the ith feature. The GCM of P under F, GF, is defined by GF(i, j) = number of pairs, pk, pl such that F(pk, pl) is true and di and dj are the descriptions of pk and pl, respectively. We discuss features derived from GCM's and present an experimental study using natural textures
Beschreibung:Date Completed 02.10.2012
Date Revised 12.11.2019
published: Print
Citation Status PubMed-not-MEDLINE
ISSN:1939-3539