| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MBE, NC (Joint) |
2011-11-24 16:10 |
Miyagi | ECEI Departments, Graduate School of Engineering, Tohoku University | An image restoration method for Poisson observation using a latent variational approximation Hayaru Shouno (UEC), Ken Takiyama, Masato Okada (The Univ. of Tokyo)  NC2011-73 |
In this study, we treat an image restoration problem throughout a Poisson noise channel observation. The Poisson noise c... [more] | NC2011-73 pp.11-16 |
| PRMU | 2011-11-25 13:45 |
Nagasaki | Face recognition based on separable lattice 2-D HMMs with variational Bayesian method Kei Sawada, Akira Tamamori, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) PRMU2011-120 |
This paper proposes an image recognition technique based on separable lattice 2-D hidden Markov models (SL2D-HMMs) with ... [more] | PRMU2011-120 pp.125-130 |
|
| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Generalization of Matrix Factorization for Robust PCA Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) IBISML2011-61 |
Principal component analysis (PCA) can be regarded as approximating a data matrix with a low-rank one by imposing spar... [more] |
IBISML2011-61 pp.127-134 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image Segmentation and Restoration using Switching State-Space Model and Variational Bayesian Method Ryota Hasegawa (Kansai Univ.), Ken Takiyama, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-67 |
We derive a deterministic algorithm that restores and segments image using switching state-space model and variational B... [more] | IBISML2011-67 pp.169-174 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image segmentation and restoration by variational Bayesian method and MCMC Kenta Kayano (Kansai Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-68 |
In this paper, we derive a deterministic algorithm that restores and segments an image by using variational Bayesian met... [more] | IBISML2011-68 pp.175-180 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image Restoration and Segmentation Based on Compound Gaussian Markov Random Field Extended as Mixture Model Takayuki Katsuki, Masato Inoue (Waseda Univ.) IBISML2011-75 |
This report proposes an accurate image restoration and segmentation using a new image model. The model is a compound Gau... [more] | IBISML2011-75 pp.223-230 |
| NC | 2011-07-25 13:45 |
Hyogo | Graduate School of Engineering, Kobe University | General Framework for Local Variational Approximation of Bayesian Learning Using Bregman Divergence Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2011-25 |
The local variational method is a technique to approximate an intractable posterior distribution in Bayesian learning. T... [more] | NC2011-25 pp.25-30 |
| NC | 2011-07-26 11:00 |
Hyogo | Graduate School of Engineering, Kobe University | Image Segmentation and Restoration using Region-Based Hidden Variables and Belief Propagation Ryota Hasegawa (Kansai Univ.), Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) NC2011-35 |
We derive a deterministic algorithm that restores and segments an image using belief propagation and a variational Bayes... [more] | NC2011-35 pp.81-86 |
| IBISML | 2010-11-05 15:30 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
Global Analytic Solution for Variational Bayesian Matrix Factorization and its Model-induced Regularization Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Ryota Tomioka (Univ. of Tokyo) IBISML2010-99 |
Bayesian methods of matrix factorization (MF) have been actively explored recently as promising alternatives to classic... [more] |
IBISML2010-99 pp.291-302 |
| 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 14:35 |
Tokyo | Tamagawa University | Information Divergences in Local Variational Approximation of Bayesian Posterior Distribution Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2009-138 |
Local variational method is a technique to approximate intractable posterior distributions in Bayesian learning. In thi... [more] |
NC2009-138 pp.297-302 |
| PRMU, SP, MVE, CQ | 2010-01-21 11:40 |
Kyoto | Kyoto Univ. | Online speaker clustering using an ergodic HMM and its application to meeting minute generation Takafumi Koshinaka, Kentaro Nagatomo, Kenji Satoh (NEC Corp.) CQ2009-62 PRMU2009-161 SP2009-102 MVE2009-84 |
A novel online speaker clustering method suitable for real-time applications is proposed. Using an ergodic hidden Marko... [more] | CQ2009-62 PRMU2009-161 SP2009-102 MVE2009-84 pp.39-44 |
| NC, MBE (Joint) |
2009-03-12 13:50 |
Tokyo | Tamagawa Univ. | Model Learning of Normalized Gaussian Networks Using On-line Information Bottleneck EM Algorithm Satoshi Imai, Hiroyuki Seki (Nara Inst. of Sci and Tech.) NC2008-143 |
In this report, we propose a new learning method of stochastic models which have hidden variables. This method estimate... [more] |
NC2008-143 pp.237-242 |
| NC, MBE (Joint) |
2009-03-13 15:40 |
Tokyo | Tamagawa Univ. | Sparse Bayesian Learning of Expansion Filters for Color Images Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2008-174 |
Classical methods for image expansion such as bicubic interpolation and splines can be understood as linear filters, who... [more] | NC2008-174 pp.417-422 |
| PRMU | 2009-02-20 11:15 |
Tokyo | Univ. of Tokyo (IIS) | Automatically Estimating Number of Scenes for Video Summarization using Model Selection Criteria Koji Yamasaki, Koichi Shinoda, Sadaoki Furui (Tokyo Inst. of Tech.) PRMU2008-231 |
This paper describes a video summarization system using model selection techniques to estimate the optimal number of sce... [more] | PRMU2008-231 pp.139-144 |
| NC, MBE (Joint) |
2008-12-20 10:00 |
Aichi | Nagoya Inst. Tech. | Clustering complex networks with the prior based on degree distribution Naoyuki Harada, Ichiro Takeuchi (NIT), Ryohei Nakano (Chubu Univ.) NC2008-73 |
Newman et al. proposed a graph clustering method based on a robabilistic mixture model with only the general assumption ... [more] | NC2008-73 pp.1-6 |
| NC | 2007-03-15 15:30 |
Tokyo | Tamagawa University | Deterministic Annealing in Variational Baysian Algorithm Kentaro Katahira (Univ. Tokyo/RIKEN), Kazuho Watanabe (Tokyo Tech), Masato Okada (Univ. Tokyo/RIKEN) |
Variational Bayes (VB) algorithm is widely used as an approximation of Bayesian method. The VB algorithm can approximate... [more] | NC2006-183 pp.177-182 |
| NC | 2006-03-15 11:00 |
Tokyo | Tamagawa University | Analysis of Hierarchical Variational Bayes Approach in Linear Inverse Problem Shinichi Nakajima, Sumio Watanabe (Tokyo Inst. of Tech./Nikon) |
It is known that, in singular models, the Bayes estimation commonly has the advantage of generalization performance o... [more] |
NC2005-117 pp.67-72 |
[Return to Top Page]