Detecting regions of dynamic texture

Tomer Amiaz, Sándor Fazekas, Dmitry Chetverikov, Nahum Kiryati

Research output: Conference contribution

29 Citations (Scopus)

Abstract

Motion estimation is usually based on the brightness constancy assumption. This assumption holds well for rigid objects with a Lambertian surface, but it is less appropriate for fluid and gaseous materials. For these materials a variant of this assumption, which we call the brightness conservation assumption should be employed. Under this assumption an object's brightness can diffuse to its neighborhood. We propose a method for detecting regions of dynamic texture in image sequences. Segmentation into regions of static and dynamic texture is achieved by using a level set scheme. The level set function separates the images into areas obeying brightness constancy and those which obey brightness conservation. Experimental results on challenging image sequences demonstrate the success of the segmentation scheme and validate the model.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision, First International Conference, SSVM 2007, Proceedings
Pages848-859
Number of pages12
Publication statusPublished - dec. 24 2007
Event1st International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2007 - Ischia, Italy
Duration: máj. 30 2007jún. 2 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4485 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2007
CountryItaly
CityIschia
Period5/30/076/2/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Amiaz, T., Fazekas, S., Chetverikov, D., & Kiryati, N. (2007). Detecting regions of dynamic texture. In Scale Space and Variational Methods in Computer Vision, First International Conference, SSVM 2007, Proceedings (pp. 848-859). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4485 LNCS).