Adaptive piecewise elastic motion estimation

Huijun Di*, Linmi Tao, Guangyou Xu

*Corresponding author for this work

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

Abstract

This paper proposes a novel probabilistic graphical model, called MTHMM-P, for partitioning general nonrigid motion into piecewise elastic motion, so as to achieve nonrigid motion estimation without the need of any a priori shape model. To this end, two interrelated sub-problems have to be addressed: partitioning whole motion sequence into several coherent pieces and estimating elastic motion inside each one. The proposed MTHMM-P jointly formulates these two sub-problems. By means of a competition-cooperation partition mechanism and joint inference algorithm, the MTHMM-P can automatically determine the number of pieces as well as adjust the span of each piece by adapting to the input sequence, and at the same time achieve the estimation of piecewise elastic motion. Experiments on the motion of face and body show the capability of the MTHMM-P.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Methodologies - 11th International Conference, ICIC 2015, Proceedings
EditorsAbir Hussain, De-Shuang Huang, Kang-Hyun Jo
PublisherSpringer Verlag
Pages203-215
Number of pages13
ISBN (Print)9783319221854
DOIs
Publication statusPublished - 2015
Event11th International Conference on Intelligent Computing, ICIC 2015 - Fuzhou, China
Duration: 20 Aug 201523 Aug 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9226
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Intelligent Computing, ICIC 2015
Country/TerritoryChina
CityFuzhou
Period20/08/1523/08/15

Keywords

  • Elastic motion estimation
  • Probabilistic graphical model

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Cite this

Di, H., Tao, L., & Xu, G. (2015). Adaptive piecewise elastic motion estimation. In A. Hussain, D.-S. Huang, & K.-H. Jo (Eds.), Intelligent Computing Theories and Methodologies - 11th International Conference, ICIC 2015, Proceedings (pp. 203-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9226). Springer Verlag. https://doi.org/10.1007/978-3-319-22186-1_21