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Combined Geometric-Texture Image Classification

  • Jean-François Aujol
  • Tony Chan
Conference paper
  • 979 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)

Abstract

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.

Keywords

Classification texture geometrical image decomposition logic model level-set active contour PDE wavelets 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jean-François Aujol
    • 1
  • Tony Chan
    • 2
  1. 1.CMLA (CNRS UMR 8536) – ENS CachanFrance
  2. 2.Dean, Division of Physical ScienceCollege of Letters and Science, UCLAUSA

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