Structure-texture decomposition by a TV-gabor model

Jean François Aujol*, Guy Gilboa, Tony Chan, Stanley Osher

*Corresponding author for this work

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

5 Scopus citations

Abstract

This paper explores new aspects of the image decomposition problem using modern variational techniques. We aim at splitting an original image f into two components u and v, where u holds the geometrical information and v holds the textural information. Our aim is to provide the necessary variational tools and suggest the suitable functional spaces to extract specific types of textures. Our modeling uses the total-variation semi-norm for extracting the structural part and a new tunable norm, presented here for the first time, based on Gabor functions, for the textural part. A way to select the splitting parameter based on the orthogonality of structure and texture is also suggested.

Original languageEnglish (US)
Title of host publicationVariational, Geometric, and Level Set Methods in Computer Vision - Third International Workshop, VLSM 2005, Proceedings
Pages85-96
Number of pages12
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

  • BV
  • Gabor functions
  • Hilbert space
  • Image decomposition
  • Projection
  • Total-variation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Structure-texture decomposition by a TV-gabor model'. Together they form a unique fingerprint.

Cite this