IEICE Technical Committee Submission System
Conference Schedule
Online Proceedings
[Sign in]
Tech. Rep. Archives
    [Japanese] / [English] 
( Committee/Place/Topics  ) --Press->
 
( Paper Keywords:  /  Column:Title Auth. Affi. Abst. Keyword ) --Press->

All Technical Committee Conferences  (Searched in: All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 17 of 17  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2022-03-09
14:55
Online Online Infinite SCAN: Joint Estimation of Changes and the Number of Word Senses with Gaussian Markov Random Fields
Seiichi Inoue, Mamoru Komachi (TMU), Toshinobu Ogiso (NINJAL), Hiroya Takamura (AIST), Daichi Mochihashi (ISM) IBISML2021-47
In this study, we propose a hierarchical Bayesian model that can automatically estimate the number of senses for each wo... [more] IBISML2021-47
pp.61-68
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Development of the topology-preserving image alignment based on a local deformation of kernel density function
Masataka Watajima (KIT), Sayuri Kuge, Takeshi Ishihara (KU), Yuichi Iino (UT), Ryo Yoshida (ISM), Terumasa Tokunaga (KIT) IBISML2018-55
Image alignment or image registration is a process for aligning two or more images into a common coordinate system. Many... [more] IBISML2018-55
pp.83-90
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Lattice Model Selection of Gaussian Markov Random Field
Hirosato Ito, Hirotaka Sakamoto, Shun Katakami, Masato Okada (Univ. Tokyo) IBISML2018-69
Recently, many observed images have been obtained in various natural scientific fields. It is very important issue to e... [more] IBISML2018-69
pp.191-196
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Structure Learning of Graph Product Multilayer Network-shaped Gaussian Markov Random Fields
Yuya Takashina, Masato Inoue (Waseda Univ.) IBISML2017-88
Learning the structure of graphical models is important in many fields, e.g., multivariate analysis and anomaly detectio... [more] IBISML2017-88
pp.383-388
PRMU 2014-06-20
14:45
Tokyo   An experiment of Text/Non-text Classification using Conditional Random Fields for Japanese Online Handwritten Japanese Ink Documents
Soichiro Inatani, Truyen Van Phan, Masaki Nakagawa (Tokyo Univ. of Agri. & Tech.) PRMU2014-34
In this paper, we propose the method based on Conditional Random fields (CRF) for separating text and non-text ink strok... [more] PRMU2014-34
pp.73-78
MI 2013-11-07
15:20
Hiroshima   Image Segmentation for NBI Endoscopy Image
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Yoko Kominami, Rie Miyaki, Taiji Matsuo, Shigeto Yoshida, Shinji Tanaka (Hiroshima Univ.) MI2013-53
In this paper, we propose a texture image segmentation method by using SVM posterior probabilities with a Markov Random ... [more] MI2013-53
pp.39-43
NC, MBE
(Joint)
2012-12-12
15:40
Aichi Toyohashi University of Technology Distribution estimation of hyperparameters in Markov random field model
Yoshinori Ohno, Kenji Nagata (Univ. Tokyo), Hayaru Shouno (UEC), Masato Okada (Univ. Tokyo/RIKEN) NC2012-86
Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science... [more] NC2012-86
pp.55-60
PRMU, SP 2012-02-09
16:30
Miyagi   [Poster Presentation] Recognition for NBI Videoendoscopy Using MRF
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shigeto Yoshida, Shinji Tanaka (Hiroshima Univ.) PRMU2011-207 SP2011-122
In this paper, we introduce temporal labeling using Markov Random Fields(MRF) to smooth labels for NBI video endoscopy.F... [more] PRMU2011-207 SP2011-122
pp.115-116
PRMU 2011-11-25
14:15
Nagasaki   Face recognition based on hidden conditional random fields using structure of separable lattice HMMs
Keisuke Kumaki, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) PRMU2011-121
In image recognition, it needs to deal with geometrical variations of an object, e.g. location, size, and etc. Separable... [more] PRMU2011-121
pp.131-136
MBE, NC
(Joint)
2010-11-18
13:30
Miyagi Tohoku University Loopy Belief Propagation for Sparse Markov Random Fields
Kazuyuki Tanaka (Tohoku Univ.) NC2010-52
In the present paper, new loopy belief propagations for sparse Markov random fields are proposed.
Our schenmes are base... [more]
NC2010-52
pp.1-5
IBISML 2010-11-04
15:00
Tokyo IIS, Univ. of Tokyo [Poster Presentation] Probabilistic Inference by minimizing the TRW Free Energy using CCCP
Yu Nishiyama (RIKEN), Xingyao Ye, Alan L. Yuille (UCLA) IBISML2010-66
We propose a family of convergent double-loop algorithms which minimize the TRW free energy. These algorithms are based ... [more] IBISML2010-66
pp.51-58
IBISML 2010-11-04
15:00
Tokyo IIS, Univ. of Tokyo [Poster Presentation] Advanced Susceptibility Propagation
Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) IBISML2010-67
Inferences in Markov random fields are ones of the most important problems in information science.
Susceptibility prop... [more]
IBISML2010-67
pp.59-63
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)
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
 Results 1 - 17 of 17  /   
Choose a download format for default settings. [NEW !!]
Text format pLaTeX format CSV format BibTeX format
Copyright and reproduction : All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan