Combined geometric-texture image classification

Jean François Aujol*, Tony Chan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationVariational, Geometric, and Level Set Methods in Computer Vision - Third International Workshop, VLSM 2005, Proceedings
Pages161-172
Number of pages12
DOIs
StatePublished - Dec 1 2005
Event3rd International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision, VLSM 2005 - Beijing, China
Duration: Oct 16 2005Oct 16 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3752 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision, VLSM 2005
CountryChina
CityBeijing
Period10/16/0510/16/05

Keywords

  • Active contour
  • Classification
  • Decomposition
  • Geometrical image
  • Level-set
  • Logic model
  • PDE
  • Texture
  • Wavelets

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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