Texture Classification and Verification Using Bispectral Estimates at All the Frequencies on a Lattice
2006
Digitized texture images can often be considered as realisations of stationary
random fields and their estimated normalised bispectra at diagonal frequencies
have been used for classification. In this paper, we devise a classifier and a
hypothesis test using bispectral estimates at all the discrete frequencies
on a lattice. For flexibility textures in each class are allowed to have a certain
amount of variation in theoretical normalised bispectra from that of a single
training sample for the class, and we also consider the case where the texture
to be classified does not belong to any of the classes. The novelty of this
paper is in a) the flexible formulation of the problem,
b) the known asymptotic distribution of the classifier,
c) the inclusion of a verification stage and
d) the complete coverage of frequencies on a lattice.
The methodology is extendable to 4th and higher order spectra and is applicable
to random field data other than texture images.
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