IEEE Trans Image Process - Rigid-motion-invariant classification of 3-D textures.

Tópicos

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Resumo

This paper studies the problem of 3-D rigid-motion-invariant texture discrimination for discrete 3-D textures that are spatially homogeneous by modeling them as stationary Gaussian random fields. The latter property and our formulation of a 3-D rigid motion of a texture reduce the problem to the study of 3-D rotations of discrete textures. We formally develop the concept of 3-D texture rotations in the 3-D digital domain. We use this novel concept to define a "distance" between 3-D textures that remains invariant under all 3-D rigid motions of the texture. This concept of "distance" can be used for a monoscale or a multiscale 3-D rigid-motion-invariant testing of the statistical similarity of the 3-D textures. To compute the "distance" between any two rotations R(1) and R(2) of two given 3-D textures, we use the Kullback-Leibler divergence between 3-D Gaussian Markov random fields fitted to the rotated texture data. Then, the 3-D rigid-motion-invariant texture distance is the integral average, with respect to the Haar measure of the group SO(3), of all of these divergences when rotations R(1) and R(2) vary throughout SO(3). We also present an algorithm enabling the computation of the proposed 3-D rigid-motion-invariant texture distance as well as rules for 3-D rigid-motion-invariant texture discrimination/classification and experimental results demonstrating the capabilities of the proposed 3-D rigid-motion texture discrimination rules when applied in a multiscale setting, even on very general 3-D texture models.

Resumo Limpo

paper studi problem d rigidmotioninvari textur discrimin discret d textur spatial homogen model stationari gaussian random field latter properti formul d rigid motion textur reduc problem studi d rotat discret textur formal develop concept d textur rotat d digit domain use novel concept defin distanc d textur remain invari d rigid motion textur concept distanc can use monoscal multiscal d rigidmotioninvari test statist similar d textur comput distanc two rotat r r two given d textur use kullbackleibl diverg d gaussian markov random field fit rotat textur data d rigidmotioninvari textur distanc integr averag respect haar measur group diverg rotat r r vari throughout also present algorithm enabl comput propos d rigidmotioninvari textur distanc well rule d rigidmotioninvari textur discriminationclassif experiment result demonstr capabl propos d rigidmot textur discrimin rule appli multiscal set even general d textur model

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