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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
MI |
2021-07-08 14:00 |
Online |
Online |
Unsupervised deep learning with low-rank and sparse priors for blood vessel enhancement from free-breathing angiography Ryoji Ishibashi, Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2021-11 |
(To be available after the conference date) [more] |
MI2021-11 pp.11-14 |
PRMU, IPSJ-CVIM |
2020-03-17 09:45 |
Kyoto |
(Cancelled but technical report was issued) |
Deep neural network representation and learning of low-rank and sparse approximation
-- With application to celiac angiography under free breathing -- Ryohei Miyoshi, Tomoya Sakai (Nagasaki Univ.), Takashi Ohnishi, Hideaki Haneishi (Chiba Univ.) PRMU2019-91 |
Low-rank and sparse (L+S) approximation, a.k.a. stable and robust principal component analysis, is known to be suitable ... [more] |
PRMU2019-91 pp.133-138 |
MI |
2019-01-22 09:35 |
Okinawa |
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Acceleration of angiographic region enhancement based on robust principal component analysis using parallel processing Morio Kawabe, Yuri Kokura, Takashi Ohnishi, Hideyuki Kato, Yoshihiko Ooka (Chiba Univ.), Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2018-59 |
Robust principal component analysis (RPCA) can extract vessel information from consecutive digital angiographic images. ... [more] |
MI2018-59 pp.1-4 |
SIP |
2017-08-24 14:15 |
Tokyo |
Tokyo Denki University |
A DDoS Attack Detection Based on Group Sparsities of OD Flow Matrices Masaya Endo, Masao Yamagishi, Isao Yamada (Tokyo Inst. of Tech.) SIP2017-52 |
To realize effective detection schemes applicable to diverse network traffic anomalies, we first propose to use group sp... [more] |
SIP2017-52 pp.21-26 |
IE, ITS, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] |
2017-02-21 14:30 |
Hokkaido |
Hokkaido Univ. |
Minimization of mixed norm for frequency spectrum of images and its application of pattern noise decomposition Keiichiro Shirai (Shinshu Univ.), Shunsuke Ono (Tokyo Institute Tech.), Masahiro Okuda (Univ. Kitakyushu) ITS2016-57 IE2016-115 |
This paper deals with a mixed norm for complex vectors, i.e., the summation of amplitude spectrum, and its minimization ... [more] |
ITS2016-57 IE2016-115 pp.275-280 |
PRMU, CNR |
2017-02-19 11:20 |
Hokkaido |
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[Poster Presentation]
Online Algorithm for Separating a Sequence of Optical Flow Fields with Low-rank and TV Regularization Shun Ogawa, Tomoya Sakai (Nagasaki Univ.) PRMU2016-179 CNR2016-46 |
Estimation of camera egomotion and detection of moving object
both require separation of the apparent motions, a.k.a. t... [more] |
PRMU2016-179 CNR2016-46 pp.155-156 |
MI |
2016-07-25 15:45 |
Hokkaido |
Tomakomai Civic Hall |
Development of quantitative perfusion evaluation methods for obstruction of blood flow of microcirculation using a rat model of septic shock. Minori Takahashi, Takashi Ohnishi, Eizo Watanabe, Shigeto Oda, Hideaki Haneishi (Chiba Univ.) MI2016-39 |
Septic shock induces organ dysfunction by microcirculatory disturbance. Observation and quantification of microcirculati... [more] |
MI2016-39 pp.19-22 |
PRMU, MI, IE, SIP |
2014-05-22 16:20 |
Aichi |
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Signal Processing for Lung-Sound Recognition
-- Separation and Classification of Continuous and Discontinuous Adventitious Sounds -- Tomoya Sakai, Takahiro Uchida, Aeri Sakaguchi, Senya Kiyasu (Nagasaki Univ.), Sueharu Miyahara (formerly Nagasaki Univ.), Mikio Oka (Kawasaki Med. Sch.) SIP2014-11 IE2014-11 PRMU2014-11 MI2014-11 |
This paper presents signal separation techniques for pattern recognition of lung sounds. From a viewpoint of pattern rec... [more] |
SIP2014-11 IE2014-11 PRMU2014-11 MI2014-11 pp.55-60 |
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 |
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