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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 12:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Mixing Method of Remote Choral Sound Source by Component Selection Using Sparse Representation Haruki Ota, Kota Takahashi (UEC)  EA2023-117 SIP2023-164 SP2023-99 |
We are working on a technique to create choral sound sources by mixing singing sound sources recorded at different place... [more] | EA2023-117 SIP2023-164 SP2023-99 pp.327-332 |
| SIS | 2023-03-02 11:00 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning Souichiro Maruyama, Makoto Nakashizuka (CIT) SIS2022-40 |
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] | SIS2022-40 pp.1-4 |
| EMM | 2023-01-26 13:35 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Audio zero-watermarking method based on auditory spectral representation Atsuki Ichikawa, Masashi Unoki (JAIST) EMM2022-65 |
Audio zero-watermark technique creates a detection key from watermark and binary pattern generated from features of the ... [more] | EMM2022-65 pp.20-25 |
| MSS, NLP | 2022-03-29 09:40 |
Online | Online | Effects of sparse connections in spiking neural networks for unsupervised pattern recognition Hiroki Shinagawa, Kantaro Fujiwara, Gouhei Tanaka (Univ. of Tokyo) MSS2021-69 NLP2021-140 |
Recently, the spiking neural network (SNN) models, which compute using spatio-temporal information representation by neu... [more] | MSS2021-69 NLP2021-140 pp.71-76 |
| EMM | 2022-01-27 14:35 |
Online | Online | Auditory Representation of Speech Signals Using a Matching Pursuit Algorithm and Sparse Coding Dung Kim Tran, Masashi Unoki (JAIST) EMM2021-87 |
Speech signals are the natural carrier of information such as linguistic, speaker individuality, and emotions, etc. Ther... [more] | EMM2021-87 pp.19-24 |
| 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 |
| NC, MBE (Joint) |
2021-03-04 09:50 |
Online | Online | Evaluation of effect of source noise on magnetoencephalography source estimation using a structured sparse model Kai Miyazaki, Shun Nirasawa, Kazuaki Akamatsu, Yoichi Miyawaki (UEC) NC2020-56 |
Magnetoencephalography (MEG) is a method to acquire human brain activity at a high temporal resolution, but its spatial ... [more] | NC2020-56 pp.77-82 |
| IA | 2020-10-01 13:15 |
Online | Online | A Study on Recovering Network Topology with Missing Links using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2020-3 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] | IA2020-3 pp.10-13 |
| SP, EA, SIP | 2020-03-02 15:10 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
A Pattern Recognition Method Using Secure Sparse Representations in L0 Norm Minimization Takayuki Nakachi, Yitu Wang (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EA2019-130 SIP2019-132 SP2019-79 |
In this paper, we propose a privacy-preserving pattern recognition method using encrypted sparse representations in L0 n... [more] | EA2019-130 SIP2019-132 SP2019-79 pp.169-174 |
| CS, CAS | 2020-02-27 14:30 |
Kumamoto | An Estimation of Network Traffic Validation based on Sparse Coding Takayuki Nakachi, Yitu Wang (NTT) CAS2019-107 CS2019-107 |
With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligen... [more] | CAS2019-107 CS2019-107 pp.55-60 |
|
| NC, MBE | 2019-12-06 15:40 |
Aichi | Toyohashi Tech | Prevention of redundant representations and of the black box in stacked autoencoders Masumi Ishikawa (Kyutech) MBE2019-56 NC2019-47 |
Recent progress in deep learning (DL) is remarkable and its recognition capability is said to surpass that of humans. Th... [more] | MBE2019-56 NC2019-47 pp.67-72 |
| IE, CS, IPSJ-AVM, ITE-BCT [detail] | 2019-12-05 11:40 |
Iwate | Aiina Center | [Special Talk]
Representation of moving-image's sparsity and its applications to adaptive moving-image restoration Takahiro Saito (Kanagawa Univ.) CS2019-75 IE2019-55 |
This talk states that statistical sparsity of a moving-image sequence can be properly represented in the domain of the 3... [more] | CS2019-75 IE2019-55 pp.29-34 |
| ITE-BCT, SIS | 2019-10-25 11:10 |
Fukui | Fukui International Activities Plaza | Image Compression in Encryption-then-Compression System Using Secure Sparse Representations Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) SIS2019-21 |
n this paper, we propose a image compression method using secure sparse representations in Encryption-then-Compression (... [more] | SIS2019-21 pp.77-82 |
| IMQ | 2019-10-04 14:00 |
Osaka | Osaka University | 3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6 |
By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If ... [more] | IMQ2019-6 pp.1-10 |
| IE, EMM, LOIS, IEE-CMN, ITE-ME, IPSJ-AVM [detail] | 2019-09-19 15:10 |
Niigata | Tokimeito, Niigata University | Secure sparse representations in L0 norm minimization Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) LOIS2019-11 IE2019-24 EMM2019-68 |
In this paper, we propose a method to estimate secure sparse representations in L0 norm minimization, and evaluate the e... [more] | LOIS2019-11 IE2019-24 EMM2019-68 pp.25-30 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 17:00 |
Okinawa | Okinawa Institute of Science and Technology | Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series Yoshikazu Shibahara, Takuto Sakuma (NIT), Ichiro Takeuchi (NIT/RIKEN/NIMS), Masayuki Karasuyama (NIT/NIMS) IBISML2019-9 |
In recent years, improvement of sensor performance and spread of portable devices such as smartphones enable us to easil... [more] | IBISML2019-9 pp.57-64 |
| NC, MBE (Joint) |
2019-03-06 15:50 |
Tokyo | University of Electro Communications | PET Image Reconstruction by use of Dictionary Learning Naohiro OKumura, Hayaru Shouno (UEC) NC2018-85 |
Nowadays, Positron Emission Tomography (PET) scan is focused in the field of pathological diagnosis.In order to obtain a... [more] | NC2018-85 pp.221-226 |
| MICT, MI | 2018-11-06 14:50 |
Hyogo | University of Hyogo | Feature extraction of coarse/fine crackles and its improvement via sparse modeling techniques Kosei Nishitsuji, Tomoya Sakai, Toshikazu Fukumitsu, Yasushi Obase (Nagasaki Univ.), Sueharu Miyahara (BIPS) MICT2018-48 MI2018-48 |
Medical experts have heuristically defined lung sound features and validated their relations with patients’ conditions i... [more] | MICT2018-48 MI2018-48 pp.45-48 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Estimation of Sparse Basis Representation for Non-periodic data Shun Katakami, Hirotaka Sakamoto, Yasuhiko Igarashi, Masato Okada (Univ. Tokyo) IBISML2018-71 |
In this research, we propose a method to estimate a sparse basis representation for non-periodic data. For periodic data... [more] | IBISML2018-71 pp.205-212 |
| NLC, IPSJ-DC | 2018-09-06 17:20 |
Tokyo | Seikei University | Latent co-occurrence words graph extraction using sparse structure estimation
-- Comparison of word vectors between topic model and distributed representation -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER) NLC2018-19 |
We are considering application of "structural topic model" in order to extract customer insight from member questionnai... [more] | NLC2018-19 pp.51-56 |
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