Presentation 2010-11-30
Particle Filter and MCMC for Communication Science
Kazuhiro OTSUKA,
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Abstract(in English) In recent years, the automatic analysis of human-human communication scenes has been acknowledged as an emerging research field. Authors have been working on multimodal conversation scene analysis that aims to provide the automatic description of conversation scenes from the multimodal nonverbal behaviors of participants, which are captured with cameras and microphones. This talk introduces two example techniques developed by author's group. The one is the probabilistic modeling and inference of conversation structures. The other is the visual tracking of the head poses of conversation participants. This talk also overviews the particle filtering and Markov Chain Monte Carlo (MCMC) method, which are used in our conversation scene analysis.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Particle Filter / Sequential Monte Carlo / Markov Chain Monte Carlo / Probabilistic Inference / Communication Science / Conversation Scene Analysis / Target Tracking / Multimodal Interaction
Paper # IT2010-52
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Committee IT
Conference Date 2010/11/23(1days)
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Registration To Information Theory (IT)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Particle Filter and MCMC for Communication Science
Sub Title (in English)
Keyword(1) Particle Filter
Keyword(2) Sequential Monte Carlo
Keyword(3) Markov Chain Monte Carlo
Keyword(4) Probabilistic Inference
Keyword(5) Communication Science
Keyword(6) Conversation Scene Analysis
Keyword(7) Target Tracking
Keyword(8) Multimodal Interaction
1st Author's Name Kazuhiro OTSUKA
1st Author's Affiliation Nippon Telegraph and Telephone Corporation, NTT Communication Science Laboratories()
Date 2010-11-30
Paper # IT2010-52
Volume (vol) vol.110
Number (no) 320
Page pp.pp.-
#Pages 10
Date of Issue