Bayesian dense motion field estimation with landmark constraint

Yi Chin*, Chun-Jen Tsai

*Corresponding author for this work

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

Abstract

In this paper, a dense motion field estimation technique based on the Bayesian framework is proposed to estimate the true dense motion fields of video sequences. Previous stochastic techniques of dense motion field estimation adopts piecewise smooth motion model and use MAP estimation to find the motion field with joint minimization of motion compensation errors and maximization of motion smoothness. However, such random process does not guarantee to converge to the true motion field. In this paper, the motion of landmark points in the video sequence is introduced into the MAP estimation process to regularize the estimated motion field. Experimental results show that the proposed algorithm produces estimated motion fields which preserve piecewise smooth nature and are visually close to the true motion of the video sequences.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages773-776
Number of pages4
DOIs
StatePublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period26/09/1029/09/10

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