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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, NLP, IPSJ-BIO [detail] | 2010-06-18 15:35 |
Okinawa | Ryukyu-daigaku-gozyu-syunen-kinenn-kaikan | Bayesian Image Super-Resoluion of Linear Degradation Model with a Compound Markov Random Field Prior Takayuki Katsuki, Akira Torii, Masato Inoue (Waseda Univ.)  NLP2010-10 NC2010-10 |
Super-resolution is a technique to estimate higher resolution image from multiple low-resolution observed images. We tre... [more] | NLP2010-10 NC2010-10 pp.63-68 |
| NC, MBE (Joint) |
2010-03-10 09:00 |
Tokyo | Tamagawa University | Inference of Alpha Rhythm Phase and Amplitude Using Belief Propagation on Markov Random Field Model Yasushi Naruse (NICT), Ken Takiyama (Univ. of Tokyo.), Masato Okada (Univ. of Tokyo/RIKEN), Tsutomu Murata (NICT) NC2009-116 |
The alpha rhythm is a major component of spontaneous electroencephalographic data. We develop a novel method that can es... [more] | NC2009-116 pp.167-172 |
| NC | 2009-10-24 10:15 |
Saga | Saga University | Construction of the maximizer of posterior marginal estimate by Langevin equation in probabilistic image processing Wataru Norimatsu, Jun-ichi Inoue (Hokkaido Univ.) NC2009-43 |
We formulate the maximizer of posterior marginal (MPM) estimate for Bayesian probabilistic image processing by using the... [more] | NC2009-43 pp.35-40 |
| NC | 2009-10-24 10:40 |
Saga | Saga University | Mean-field theoretical approach to Bayesian estimation of motion velocity vector in successive digital images Yuya Inagaki, Jun-ichi Inoue (Hokkaido Univ.) NC2009-44 |
We examine a mean-field iterative aigorithm to estimate motion velocity vector fields in successive digital images based... [more] | NC2009-44 pp.41-46 |
| NC, MBE (Joint) |
2009-03-12 14:15 |
Tokyo | Tamagawa Univ. | Learning algorithm in Boltzmann machines using the belief propagation algorithm Junya Tannai, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2008-144 |
Boltzmann machines are stochastic neural networks defined on undirected graphs and are expected to be learning machines ... [more] | NC2008-144 pp.243-248 |
| NC, MBE (Joint) |
2008-12-20 10:25 |
Aichi | Nagoya Inst. Tech. | Shape from shading based on a probabilistic model including surface orientation and depth fields Yuki Nakatsuji, Toshiyuki Tanaka (Kyoto Univ) NC2008-74 |
In this paper, we propose a new method to solve shape-from-shading problems based on a probabilistic model. The propos... [more] |
NC2008-74 pp.7-12 |
| NC, MBE (Joint) |
2008-03-12 13:30 |
Tokyo | Tamagawa Univ | Image Processing by using the EM algorithm and the belief propagation Kei Inoue, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2007-118 |
Markov random fields in image processing includehyperparameters to estimate from given data. We introduce a method to es... [more] | NC2007-118 pp.37-42 |
| PRMU, HIP | 2007-02-22 16:15 |
Kanagawa | Online Action Recognition with Structured Boosting Yu Nejigane, Masamichi Shimosaka, Taketoshi Mori, Tomomasa Sato (Tokyo Univ.) |
In this paper, we propose a robust online action recognition method based on boosted sequential classification. Our meth... [more] | PRMU2006-216 HIP2006-109 pp.59-64 |
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