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 |