Phase transitions and the perceptual organization of video sequences

Yair Weiss*

*Corresponding author for this work

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

3 Scopus citations

Abstract

Estimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision. A promising approach to this problem involves using mixture models, where the motion of each object is a component in the mixture. However, existing methods typically require specifying in advance the number of components in the mixture, i.e. the number of objects in the scene. Here we show that the number of objects can be estimated automatically in a maximum likelihood framework, given an assumption about the level of noise in the video sequence. We derive analytical results showing the number of models which maximize the likelihood for a given noise level in a given sequence. We illustrate these results on a real video sequence, showing how the phase transitions correspond to different perceptual organizations of the scene.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Pages850-856
Number of pages7
ISBN (Print)0262100762, 9780262100762
StatePublished - 1998
Externally publishedYes
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: 1 Dec 19976 Dec 1997

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Country/TerritoryUnited States
CityDenver, CO
Period1/12/976/12/97

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