| 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 # | |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-16 13:10 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
[Special Talk]
Group Sparse/Low-rank Modeling for Multidimensional Signal Recovery Seisuke Kyochi (Kogakuin Univ.)  IMQ2022-52 CQ2022-93 IE2022-129 MVE2022-82 |
Group sparse/low-rank modeling based on the ℓ1 norm and nuclear norm has been successfully applied to signal processing... [more] |
IMQ2022-52 CQ2022-93 IE2022-129 MVE2022-82 pp.156-161(IMQ), pp.64-69(CQ), pp.156-161(IE), pp.156-161(MVE) |
| IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2021-06-03 17:00 |
Online | Online | Quadratic Surface Clustering Based on Hyperslab Projection and Its Application to Color Artifact Removal Tatsuya Ohtsubo, Seisuke Kyochi (The Univ. of Kitakyushu) SIP2021-5 BioX2021-5 IE2021-5 |
Color artifact removal based on local color nuclear norm (LCNN) has been proposed conventionally. LCNN relies on the fac... [more] | SIP2021-5 BioX2021-5 IE2021-5 pp.21-26 |
| IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2021-06-04 11:45 |
Online | Online | Frequency-domain Robust Principal Component Analysis Manabu Sueyasu, Seisuke Kyochi (The Univ. of Kitakyushu), Shunsuke Ono (Tokyo Tech) SIP2021-10 BioX2021-10 IE2021-10 |
Low-rank modeling is one of the most effective approaches that can analyze and extract isomorphic components from an obs... [more] | SIP2021-10 BioX2021-10 IE2021-10 pp.46-50 |
| 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 |
| SIP, MI, IE | 2019-05-23 13:00 |
Aichi | Optimum 2-D Sparse Array with Extended Aperture for Accurate DOA Estimation Shogo Nakamura, Sho Iwazaki, Koichi Ichige (Yokohama Nat'l Univ.) SIP2019-1 IE2019-1 MI2019-1 |
We present a way of optimizing 2-D sparse array with extended aperture and its interpolation method based on simulated a... [more] | SIP2019-1 IE2019-1 MI2019-1 pp.1-6 |
|
| MI, MICT | 2016-09-16 14:25 |
Tokyo | Koganei Campus, Tokyo University of Agriculture and Technology | [Invited Talk]
Tensor Completion based on Low-Rank and Smooth Structures Tatsuya Yokota (NITECH) MICT2016-42 MI2016-56 |
Completion is a procedure that facilitates the estimation of the values of missing elements of array data, using only th... [more] | MICT2016-42 MI2016-56 pp.35-40 |
| PRMU, CNR | 2014-02-14 10:40 |
Fukuoka | [Special Talk]
Convex Optimization for Applications to Image Analysis and Processing
-- With a focus on effective utilization of the nuclear norm -- Shunsuke Ono, Masao Yamagishi, Isao Yamada (Tokyo Inst. of Tech.) PRMU2013-158 CNR2013-66 |
Remarkable advances in convex optimization have enabled us to handle large-scale (constrained) convex optimization probl... [more] | PRMU2013-158 CNR2013-66 p.147 |
|
| SIP | 2013-08-29 14:10 |
Tokyo | Tokyo University of Agriculture and Technology | Overlapped Local Color Nuclear Norm And Its Constrained Use Shunsuke Ono, Isao Yamada (Tokyo Inst. of Tech.) SIP2013-68 |
This report presents a generalization of the local color nuclear norm (G-LCNN) and proposes its constrained use for colo... [more] | SIP2013-68 pp.13-16 |
| SP, EA, SIP | 2013-05-17 17:15 |
Okayama | Detecting Reflection from Single Image Based on Nuclear Norm Minimization Katsuhiro Toyokawa, Shunsuke Ono, Masao Yamagishi, Isao Yamada (Tokyo Inst. of Tech.) EA2013-29 SIP2013-29 SP2013-29 |
We propose a reflection detection method, namely, a method to extract edges of reflection, from a single image using a c... [more] | EA2013-29 SIP2013-29 SP2013-29 pp.169-173 |
|
[Return to Top Page]