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Issue No. 03 - March (1979 vol. 1)
ISSN: 0162-8828
pp: 251-259
Steven A. Johns , Abbott Laboratories, Dallas, TX.
J. K. Aggarwal , FELLOW, IEEE, Department of Electrical Engineering, University of Texas at Austin, Austin, TX 78712.
Larry S. Davis , Department of Computer Science, University of Texas at Austin, Austin, TX 78712.
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.
Steven A. Johns, J. K. Aggarwal, Larry S. Davis, "Texture Analysis Using Generalized Co-Occurrence Matrices", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 1, no. , pp. 251-259, March 1979, doi:10.1109/TPAMI.1979.4766921
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