Dynamic texture detection based on motion analysis

Sándor Fazekas, Tomer Amiaz, Dmitry Chetverikov, Nahum Kiryati

Research output: Contribution to journalArticle

38 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 an alternative assumption is required. This work examines three possible alternatives: gradient constancy, color constancy and brightness conservation (under this assumption the brightness of an object can diffuse to its neighborhood). Brightness conservation and color constancy are found to be adequate models. We propose a method for detecting regions of dynamic texture in image sequences. Accurate segmentation into regions of static and dynamic texture is achieved using a level set scheme. The level set function separates each image into regions that obey brightness constancy and regions that obey the alternative assumption. We show that the method can be simplified to obtain a less robust but fast algorithm, capable of real-time performance. Experimental results demonstrate accurate segmentation by the full level set scheme, as well as by the simplified method. The experiments included challenging image sequences, in which color or geometry cues by themselves would be insufficient.

Original languageEnglish
Pages (from-to)48-63
Number of pages16
JournalInternational Journal of Computer Vision
Volume82
Issue number1
DOIs
Publication statusPublished - Apr 1 2009

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Keywords

  • Brightness conservation
  • Dynamic texture
  • Level set
  • Motion analysis
  • Optical flow
  • Real-time processing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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