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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CCS, NLP | 2022-06-09 14:55 |
Osaka | (Primary: On-site, Secondary: Online) |
Basic Performance of CNNs Using Dynamic Filters Based on Octave Convolution Kiyotaka Matono, Hidehiro Nakano (Tokyo City Univ.)  NLP2022-5 CCS2022-5 |
The methods of using dynamic filters for convolutional neural networks (CNNs) have attracted attentions. In recent years... [more] | NLP2022-5 CCS2022-5 pp.23-26 |
| RECONF | 2022-06-08 15:25 |
Ibaraki | CCS, Univ. of Tsukuba (Primary: On-site, Secondary: Online) |
A Compact High-Speed CNN Implementation based on Redundant Computational Analysis and FPGA Acceleration Li Qi, Li Hengyi, Meng Lin (Ritsumeikan Univ.) RECONF2022-21 |
Convolutional Neural Networks (CNNs) have achieved high performance and are widely used in various applications. However... [more] | RECONF2022-21 pp.89-94 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:40 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) SIP2022-29 BioX2022-29 IE2022-29 MI2022-29 |
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable co... [more] | SIP2022-29 BioX2022-29 IE2022-29 MI2022-29 pp.150-155 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 17:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Deformable registration of 3D medical images with Deep Residual UNet Taiga Nakamura, Yuki Sato, Hiroyuki Kudo, Hotaka Takizawa (Univ. of Tsukuba) SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 |
(To be available after the conference date) [more] | SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 pp.156-160 |
| 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 |
| 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 |
| 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) |
| 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 |
| 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 |
| MI, MICT [detail] | 2021-11-05 10:25 |
Online | Online | Performance Improvement of Alzheimer's Disease Identification Using Cognitive Function Test Scores Daiki Endo, Koichi Ito, Takafumi Aoki (Tohoku Univ.) MICT2021-31 MI2021-29 |
As the population ages, the prevalence of Alzheimer’s disease (AD) is expected to increase. AD causes progressive brain ... [more] | MICT2021-31 MI2021-29 pp.17-21 |
| EMT, IEE-EMT | 2021-11-05 11:15 |
Online | Online | Quaternion convolutional neural networks for PolSAR land classification Yuya Matsumoto, Ryo Natsuaki, Akira Hirose (UTokyo) EMT2021-43 |
We propose a quaternion convolutional neural network (QCNN) for Polarimetric synthetic aperture radar (PolSAR) land cla... [more] |
EMT2021-43 pp.76-81 |
| RECONF | 2021-09-10 10:20 |
Online | Online | Convolutional neural network implementations using Vitis AI Akihiko Ushiroyama, Nobuya Watanabe, Akira Nagoya, Minoru Watanabe (Okayama Univ.) RECONF2021-19 |
Recently, Xilinx provides an FPGA-based Vitis AI development environment which is one of deep learning frameworks to acc... [more] | RECONF2021-19 pp.13-18 |
| CS | 2021-07-16 09:40 |
Online | Online | Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29 |
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] | CS2021-29 pp.63-68 |
| KBSE, IPSJ-SE, SS [detail] | 2021-07-08 15:15 |
Online | Online (Zoom) | A Study of Voiceprint Authentication Using Deep Learning of Image Classification Yuki Hiroi, Mengchun Xie, Nobuo Iwasaki, Mitsutoshi Murata, Toru Mori (NIT,Wakayama College) SS2021-7 KBSE2021-19 |
Currently, special fraud targeting the elderly is a problem in Japan. As a countermeasure, it is possible to identify th... [more] | SS2021-7 KBSE2021-19 pp.37-40 |
| PRMU, IPSJ-CVIM, IPSJ-NL | 2021-05-21 10:00 |
Online | Online | Scene Recognition of Omni-Directional Images by Patch-Based CNN Takumi Hatogai, Takao Yamanaka (Sophia Univ.) PRMU2021-3 |
In this paper, a scene recognition method for omni-directional images is proposed using patch-based convolutional neural... [more] | PRMU2021-3 pp.13-18 |
| PRMU, IPSJ-CVIM, IPSJ-NL | 2021-05-21 10:15 |
Online | Online | PRMU2021-4 |
In recent years, multi-task learning and multi-domain learning have been developed to improve the accuracy for each task... [more] | PRMU2021-4 pp.19-24 |
| CCS | 2021-03-29 15:40 |
Online | Online | A 3DCNN with Reduced Parameters Using Depthwise Separable Convolution Koki Ito, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-27 |
Convolutional Neural Networks (CNNs) have been used in various fields such as image and speech. In recent years, CNNs ha... [more] | CCS2020-27 pp.37-41 |
| PRMU, IPSJ-CVIM | 2021-03-04 10:30 |
Online | Online | Learning Convolutional Neural Networks with Spatial Frequency Loss Naoyuki Ichimura (AIST) PRMU2020-73 |
The pixel-wise L2 and pixel-wise L1 losses have been commonly used to measure the consistency between images in learning... [more] | PRMU2020-73 pp.25-30 |
| NC, MBE (Joint) |
2021-03-03 13:00 |
Online | Online | Hybrid Sparsity in Convolutional Neural Networks Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2020-46 |
Convolutional neural networks (CNNs) have achieved high accuracy in areas such as image classification and object detect... [more] | NC2020-46 pp.21-24 |
| NC, MBE (Joint) |
2021-03-03 13:25 |
Online | Online | Visualization of CNNs using Preferred Stimulus in Receptive Fields Genta Kobayashi, Hayaru Shouno (UEC) NC2020-47 |
Convolutional neural networks have shown high performance at image processing task, and they are interpreted by variou... [more] |
NC2020-47 pp.25-30 |
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