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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IT, ISEC, RCC, WBS | 2022-03-10 11:35 |
Online | Online | Improving accuracy by optimizing activation function for convolutional neural network using homomorphic encryption Kohei Yagyu, Ren Takeuchi, Vo Ngoc Khoi Nguyen, Masakatsu Nishigaki, Tetsushi Ohki (Shizuoka Univ.)  IT2021-92 ISEC2021-57 WBS2021-60 RCC2021-67 |
The development of secure neural network technology that performs prediction while encrypting data using homomorphic enc... [more] | IT2021-92 ISEC2021-57 WBS2021-60 RCC2021-67 pp.58-65 |
| KBSE | 2022-03-09 16:20 |
Online | Online (Zoom) | Code review support and verification of effectiveness using deep learning with images of programs Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2021-49 |
Code review is one of the ways to improve the quality of programs. Code reviews cannot point out all faults, but if rev... [more] |
KBSE2021-49 pp.48-53 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 10:10 |
Online | Online (Zoom) | A study on player and ball tracking in tennis videos. Kosuke Matsumoto (Kobe univ.), Junki Tamae (iret), Nobutaka Kuroki (Kobe univ.), Kensuke Hirano (iret), Masahiro Numa (Kobe univ.) IMQ2021-16 IE2021-78 MVE2021-45 |
This paper proposes a player and ball tracking method in tennis videos with image processing techniques. The proposed me... [more] | IMQ2021-16 IE2021-78 MVE2021-45 pp.33-38 |
| IBISML | 2022-03-09 09:05 |
Online | Online | [Invited Talk]
--- Koji Fukagata (Keio Univ.) IBISML2021-39 |
In recent years, the application of machine learning to various problems of fluid mechanics has been actively studied. I... [more] | IBISML2021-39 p.32 |
| CNR, BioX | 2022-03-03 14:00 |
Online | Online | A Study on Ear Personal Authentication System Using Spectrogram Sora Masuda (Kansai Univ.), Shunsuke Kita (ORIST), Yoshinobu Kajikawa (Kansai Univ.) BioX2021-48 CNR2021-29 |
In recent years, biometric authentication, such as fingerprint and face recognition, has become widespread in smartphone... [more] | BioX2021-48 CNR2021-29 pp.13-16 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 15:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Detection of individual beats from EEG during rhythm imagination Naoki Yoshimura, Toshihisa Tanaka (TUAT) EA2021-92 SIP2021-119 SP2021-77 |
Rhythm is one element of music and is composed of several beats. It has been reported that evenly spaced beats and imagi... [more] | EA2021-92 SIP2021-119 SP2021-77 pp.177-182 |
| MI | 2022-01-26 13:39 |
Online | Online | Deep Learning based 2D/3D deformable Image Registration for Abdominal Organs Ryuto Miura, Megumi Nakao, Mitsuhiro Nakamura, Tetsuya Matsuda (Kyoto Univ.) MI2021-62 |
2D/3D image registration is a problem that solves the deformation and alignment of a pre-treatment 3D image to a 2D proj... [more] | MI2021-62 pp.70-75 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online | Online | Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] | NC2021-44 pp.59-64 |
| RCS, SIP, IT | 2022-01-21 10:55 |
Online | Online | A lossless audio codec based on hierarchical residual prediction Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239 |
In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network a... [more] | IT2021-71 SIP2021-79 RCS2021-239 pp.239-244 |
| ICTSSL, CAS | 2022-01-20 15:00 |
Online | Online | On the Improvement of Recognition Accuracy of Road Sign Identification CNN using Flux and the Consideration of building RCNN Jihang Chang, Kazuya Ozawa, Hideaki Okazaki (SIT) CAS2021-60 ICTSSL2021-37 |
In this report，we discuss how to improve the recognition accuracy of a neural network for recognizing traffic signs usin... [more] | CAS2021-60 ICTSSL2021-37 pp.37-40 |
| EA, US (Joint) |
2021-12-22 15:50 |
Kumamoto | Sojo University | [Poster Presentation]
Basic study about CNN classification of liver fibrosis stages of ultrasonic B-mode images including their amplitude-envelope statistics Akiho Isshiki, Yuki Ujihara (Chiba Univ.), Dar-In Tai, Po-Hsiang Tsui (Chang Gung Univ.), Kenji Yoshida, Tadashi Yamaguchi, Shinnosuke Hirata (Chiba Univ.) US2021-52 |
The progression and therapeutic effect of diffuse liver disease can be quantitatively evaluated by the estimation of liv... [more] | US2021-52 pp.26-30 |
| PRMU | 2021-12-17 15:00 |
Online | Online | Toward high-accuracy CNN learning using dropout. Hirokazu Akiba, Toshikazu Wada (Wakayama Univ.) PRMU2021-52 |
(To be available after the conference date) [more] | PRMU2021-52 pp.154-159 |
| RCS, NS (Joint) |
2021-12-17 14:05 |
Nara | Nara-ken Bunka Kaikan and Online (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Performance evaluation on QoS Prediction between Terminals and Access Points Using Convolutional Neural Network Hiroya Ono, Yuki Sakaue, Satoshi Narikawa (NTT) NS2021-108 RCS2021-191 |
Recent mobile terminals have been able to choose from multiple connection options, and optimally accommodating them to e... [more] | NS2021-108 RCS2021-191 pp.59-64(NS), pp.82-87(RCS) |
| HCGSYMPO (2nd) |
2021-12-15 - 2021-12-17 |
Online | Online | Deep Transfer Strategies for Recognizing Functional Head-Movement Interactions in Multiparty Meetings Takashi Mori, Kazuhiro Otsuka (YNU) |
In group meetings, the participant's head movements play various functions. This study extends the concept of individual... [more] | |
| DC | 2021-12-10 15:25 |
Kagawa | (Primary: On-site, Secondary: Online) |
Prediction of Train Delays at Stations Using Multiple Convolutional Neural Networks with Actual Operation Data Tsukasa Takahashi, Takumi Fukuda, Sei Takahashi (Nihon Univ.), Hideo Nakamura (UTokyo) DC2021-61 |
In the metropolitan area, railroads are frequently delayed due to high congestion rates during rush hours, and many meas... [more] | DC2021-61 pp.34-37 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:35 |
Online | Online | Basic evaluation of ReNA, a DNN accelerator using numerical representation posit Yasuhiro Nakahara, Yuta Masuda, Masato Kiyama, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.) VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 |
In Convolutional Neural Network (CNN) accelerators for edge, numerical precision of data should be reduced as much as po... [more] | VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 pp.43-48 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 14:20 |
Online | Online | Convolutional Neural Network using RISC-V Koki Oshiro (UEC) VLD2021-46 ICD2021-56 DC2021-52 RECONF2021-54 |
(To be available after the conference date) [more] | VLD2021-46 ICD2021-56 DC2021-52 RECONF2021-54 pp.168-171 |
| NC, MBE (Joint) |
2021-11-26 15:25 |
Online | Online | Explaining coarse visual processing in the subcortical pathway with convolutional neural networks Chanseok Lim, Mikio Inagaki (Osaka Univ.), Takashi Shinozaki (NICT), Ichiro Fujita (Osaka Univ.) NC2021-28 |
The subcortical pathway for face processing conveys information rapidly but roughly to the amygdala. The fast processing... [more] | NC2021-28 pp.1-6 |
| IPSJ-AVM, CS, IE, ITE-BCT [detail] | 2021-11-25 11:15 |
Online | Online | CS2021-62 IE2021-21 |
A light field (LF) image is composed of multi-view images acquired by slightly offset viewpoints. We propose a novel met... [more] | CS2021-62 IE2021-21 pp.13-18 |
| MI, MICT [detail] | 2021-11-05 10:00 |
Online | Online | [Short Paper]
Prediction of therapeutic response in Sjogren's syndrome using ultrasound images of parotid glands Kohei Fujiwara, Takeda Keita, Yukinori Takagi, Miho Sasaki, Sato Eida, Ikuo Katayama, Misa Sumi, Tomoya Sakai (Nagasaki Univ.) MICT2021-30 MI2021-28 |
The purpose of this study was to predict the response to treatment of SS from ultrasound (US) images of salivary glands ... [more] | MICT2021-30 MI2021-28 pp.15-16 |
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