Presentation 2009-05-28
Real-time estimation of human visual attention with MCMC-based particle filter
Kouji MIYAZATO, Akisato KIMURA, Shigeru TAKAGI, Junji YAMATO,
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Abstract(in English) This report proposes a new method for achieving a precise estimation of human visual attention with considerably less execution time. The main contribution of this report is the incorporation of a particle filter with Markov chain Monte-Carlo (MCMC) sampling into a previously proposed stochastic model of saliency-based human visual attention. This enables us to introduce stream processing with such as graphics processing units (CPU) for the acceleration of the estmation. Experimental results indicate that the proposed method can estimate human visual attention more than 10 times faster than and as precisely as previous methods.
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Keyword(in English) Visual attention / dynamic Bayesian network / stream processing / MCMC / particle filter
Paper # IE2009-25,PRMU2009-16,MI2009-16
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Committee IE
Conference Date 2009/5/21(1days)
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Registration To Image Engineering (IE)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Real-time estimation of human visual attention with MCMC-based particle filter
Sub Title (in English)
Keyword(1) Visual attention
Keyword(2) dynamic Bayesian network
Keyword(3) stream processing
Keyword(4) MCMC
Keyword(5) particle filter
1st Author's Name Kouji MIYAZATO
1st Author's Affiliation NTT Communication Science Laboratories, NTT Corporation:Department of Information and Communication Systems Engineering, Okinawa National College of Technology()
2nd Author's Name Akisato KIMURA
2nd Author's Affiliation NTT Communication Science Laboratories, NTT Corporation
3rd Author's Name Shigeru TAKAGI
3rd Author's Affiliation Department of Information and Communication Systems Engineering, Okinawa National College of Technology
4th Author's Name Junji YAMATO
4th Author's Affiliation NTT Communication Science Laboratories, NTT Corporation
Date 2009-05-28
Paper # IE2009-25,PRMU2009-16,MI2009-16
Volume (vol) vol.109
Number (no) 63
Page pp.pp.-
#Pages 6
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