Nonrigid Structure-From-Motion From 2-D Images Using Markov Chain Monte Carlo

Huiyu Zhou, Xuelong Li, Abdul H. Sadka

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
2 Downloads (Pure)


In this paper we present a new method for simultaneously determining three dimensional (3-D) shape and motion of a non-rigid object from uncalibrated two dimensional (2- D) images without assuming the distribution characteristics. A non-rigid motion can be treated as a combination of a rigid rotation and a non-rigid deformation. To seek accurate recovery of deformable structures, we estimate the probability distribution function of the corresponding features through random sampling, incorporating an established probabilistic model. The fitting between the observation and the projection of the estimated 3-D structure will be evaluated using a Markov chain Monte Carlo based expectation maximisation algorithm. Applications of the proposed method to both synthetic and real image sequences are demonstrated with promising results.
Original languageEnglish
Article number6032104
Pages (from-to)168-177
Number of pages10
JournalIEEE Transactions on Multimedia
Issue number1
Publication statusPublished - Feb 2012

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Media Technology
  • Computer Science Applications

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