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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2021-08-26 10:00 |
Online | Online | Unsupervised non-rigid alignment for multiple noisy images Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.)  PRMU2021-7 |
We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decompositio... [more] | PRMU2021-7 pp.1-6 |
| IA, ICSS | 2021-06-22 11:15 |
Online | Online | A Solution for Recovering Missing Links in Network Topology using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-14 ICSS2021-14 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] | IA2021-14 ICSS2021-14 pp.74-79 |
| HWS, VLD [detail] | 2021-03-03 13:00 |
Online | Online | [Memorial Lecture]
Scheduling Sparse Matrix-Vector Multiplication onto Parallel Communication Architecture Mingfei Yu, Ruitao Gao, Masahiro Fujita (Univ. Tokyo) VLD2020-71 HWS2020-46 |
There is an obvious trend to make use of hardware including many-core CPU, GPU and FPGA, to conduct computationally inte... [more] | VLD2020-71 HWS2020-46 pp.24-29 |
| PRMU, IE, MI, SIP | 2017-05-25 15:10 |
Aichi | Graph Learning for Spectral Clustering using Low-rank and Sparse Decomposition Taiju Kanada, Masaki Onuki, Yuichi Tanaka (TUAT) SIP2017-10 IE2017-10 PRMU2017-10 MI2017-10 |
Spectral clustering is a method of clustering using eigenvectors of graph Laplacian. By using appropriate graphs, it is ... [more] | SIP2017-10 IE2017-10 PRMU2017-10 MI2017-10 pp.55-60 |
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| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Additive Model Decomposition with Global Sparse Structure for Multi-task Granger Causal Estimation Hitoshi Abe, Jun Sakuma (Univ. Tsukuba) IBISML2016-56 |
Causality estimation is one of the key issues in time-series data analysis. Granger causality is widely known as a form... [more] |
IBISML2016-56 pp.73-79 |
| PRMU, MI, IE, SIP | 2015-05-14 13:30 |
Mie | Robust Point Correspondence Using ICP and Sparse Low Rank Decomposition Qiaochu Zhao, Xian-Hua Han, Yen-Wei Chen (Ritsumeikan Univ.) SIP2015-5 IE2015-5 PRMU2015-5 MI2015-5 |
Registration or alignment among point cloud data is an essential issue in the field of computer vision. To solve the cor... [more] | SIP2015-5 IE2015-5 PRMU2015-5 MI2015-5 pp.23-28 |
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| MI | 2015-03-02 09:17 |
Okinawa | Hotel Miyahira | 4D-MRI Reconstruction using the low-rank plus sparse matrix decomposition Yukinojo Kitakami, Takashi Ohnishi, Yoshitada Masuda (Chiba Univ. Engineering), Koji Matsumoto (Chiba University Hospital), Hideaki Haneishi (Chiba Univ. Engineering) MI2014-54 |
4D-MRI can visualize and quantify the three-dimensional dynamics of the thoracoabdominal respiratory movement and allows... [more] | MI2014-54 pp.7-11 |
| SIP, RCS | 2013-02-01 10:50 |
Hiroshima | Viewport-Kure-Hotel (Kure) | Sparse Channel Estimation for MIMO-OFDM Amplify-and-Forward Two-Way Relay Networks Guan Gui, Fumiyuki Adachi (Tohoku Univ.) SIP2012-116 RCS2012-273 |
[more] | SIP2012-116 RCS2012-273 pp.207-212 |
| SP, EA, SIP | 2010-05-27 14:55 |
Hyogo | Konan Univ. (Hirao Seminar House) | A sparse decomposition method for mixtures of periodic signals with varying envelopes Yuta Ishii, Makoto Nakashizuka, Youji Iiguni (Osaka Univ.) EA2010-20 SIP2010-20 SP2010-20 |
This study proposes a sparse decomposition method for mixtures of periodic signals with varying envelopes. Periodic dec... [more] |
EA2010-20 SIP2010-20 SP2010-20 pp.115-120 |
| SIS | 2007-12-11 09:40 |
Hyogo | A shift-invariant non-negative sparse image representation with estimation of the number of bases Hidenari Nishiura, Makoto Nakashizuka, Youji Iiguni (Osaka Univ.) SIS2007-58 |
A sparse coding is one of the generative model for images, and can obtain bases that indicates features of an image unde... [more] | SIS2007-58 pp.1-6 |
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