Combined geometric-texture image classification - Archive ouverte HAL
Article Dans Une Revue Lecture Notes in Computer Science Année : 2006

Combined geometric-texture image classification

Tony Chan
  • Fonction : Auteur

Résumé

In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. Using a decomposition algorithm inspired by the recent work of Y. Meyer, we can get two channels from the original image to classify: one containing the geometrical information, and the other the texture. Using the logic framework by Chan and Sandberg, we can then combine the information from both channels in a user definable way. Thus, we design a classification algorithm in which the different classes are characterized both from geometrical and textured features. Moreover, the user can choose different ways to combine information.

Dates et versions

hal-00202013 , version 1 (03-01-2008)

Identifiants

Citer

Jean-François Aujol, Tony Chan. Combined geometric-texture image classification. Lecture Notes in Computer Science, 2006, Variational, Geometric, and Level Set Methods in Computer Vision (3752), p. 161-172. ⟨10.1007/11567646_14⟩. ⟨hal-00202013⟩
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