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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 09:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Regularizing Neural Networks with Meta Learning Generative Models Shin'ya Yamaguchi (NTT/Kyoto Univ.), Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto Univ.) |
(To be available after the conference date) [more] | |
| SIS | 2023-03-02 13:30 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
An image watermarking method using adversarial perturbations Sei Takano, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.) |
(To be available after the conference date) [more] | |
| RCS, SR, SRW (Joint) |
2023-03-01 10:25 |
Tokyo | Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.), Gui Guan (NJUPT) |
[more] | |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-02-28 16:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images Hien Ohnaka (NITTC), Shinnosuke Takamichi (UT), Keisuke Imoto (DU), Yuki Okamoto (Rits), Kazuki Fujii, Hiroshi Saruwatari (UT) |
(To be available after the conference date) [more] | |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 10:15 |
Hokkaido | Hokkaido Univ. | Generation Method of Targeted Adversarial Examples using Gradient Information for the Target Class of the Image Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) |
(To be available after the conference date) [more] | |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 10:45 |
Hokkaido | Hokkaido Univ. | A Study of Estimating 3D Skeleton of a Human Full-Body Using Images Acquired by Omnidirectional Cameras Attached to the Human Body and Deep Learning Yuta Arai, Jun Ohya (Waseda Univ.), Hiroyuki Ogata (Seikei Univ.), Seo Chanjin (Waseda Univ.) |
(To be available after the conference date) [more] | |
| IE | 2023-02-02 13:30 |
Tokyo | NII (Primary: On-site, Secondary: Online) |
Recognition of imperfect image based on deep learning
-- Estimating the number of 3-dimentional building blocks -- Takuto Nakazawa, Shigeru Kubota (Yamagata Univ.)  IE2022-51 |
(To be available after the conference date) [more] | IE2022-51 pp.1-5 |
| EMM | 2023-01-26 09:55 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
On the Transferability of Adversarial Examples between Isotropic Network and CNN models Miki Tanaka (Tokyo Metropolitan Univ.), Isao Echizen (NII), Hitoshi Kiya (Tokyo Metropolitan Univ.) EMM2022-62 |
Deep neural networks are well known to be vulnerable to adversarial examples (AEs). In addition, AEs generated for a sou... [more] | EMM2022-62 pp.7-12 |
| IT, RCS, SIP | 2023-01-25 10:25 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
A Fundamental Study on Decoding Short Length Polar Codes by Deep Learning Reona Kumaki, Hiroshi Tsutsui, Takeo Ohgane (Hokkaido Univ.) IT2022-52 SIP2022-103 RCS2022-231 |
LDPC codes, Turbo codes, and polar codes are currently known as the best channel codes achieving near Shannon limit.... [more] |
IT2022-52 SIP2022-103 RCS2022-231 pp.132-135 |
| AP, WPT (Joint) |
2023-01-19 11:15 |
Hiroshima | Hiroshima Institute of Technology (Primary: On-site, Secondary: Online) |
Multi-Input RNN Based Proactive Prediction of Path Loss using Building Information in UMa Environments Motoharu Sasaki, Naoki Shibuya, Kenichi Kawamura, Nobuaki Kuno, Minoru Inomata, Wataru Yamada, Takatsune Moriyama (NTT) AP2022-180 |
We report a multi-input RNN model that predicts path loss after 5 seconds using GRU (Gated Recurrent Unit), which is one... [more] | AP2022-180 pp.18-23 |
| EA, US (Joint) |
2022-12-22 13:30 |
Hiroshima | Satellite Campus Hiroshima | [Poster Presentation]
Examination of Improvement of Sound Quality of Optical Bone Conduction Speech Using Convolutional Neural Network Daiki Kawamoto, Masashi Nakayama (Hiroshima City Univ) EA2022-62 |
Optical-bone-conduction speech can be obtained by using a contact-type optical microphone. As with general bone conducti... [more] | EA2022-62 pp.7-12 |
| PRMU | 2022-12-15 10:30 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Single Image Raindrop Removal Using a Non-local Operator and Feature Maps in the Frequency Domain Shinya Ezumi, Masaaki Ikehara (Keio Univ.) PRMU2022-34 |
High-quality raindrop removal is desired for outdoor image processing systems as well as for acquiring good-looking imag... [more] | PRMU2022-34 pp.13-18 |
| PRMU | 2022-12-15 10:45 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
DN4C
-- An Interactive Image Segmentation System Combining DNN And Nearest Neighbor Classifier -- Toshikazu Wada, Koji Kamma (Wakayama University) PRMU2022-35 |
Color/texture based image segmentation can be widely applied to the images for product and/or medical inspection, remote... [more] | PRMU2022-35 pp.19-24 |
| HCGSYMPO (2nd) |
2022-12-14 - 2022-12-16 |
Kagawa | Onsite (Sunport Takamatsu) and Online (Primary: On-site, Secondary: Online) |
Computational Modeling with Geometric Morphometrics and Deep Neural Networks
-- An approach Methodology for Identifying Facial Impression Factors -- Takanori Sano, Hideaki Kawabata (Keio Univ.) |
Numerous studies have been conducted in psychology on the factors that influence facial impressions. In recent years, st... [more] | |
| HCGSYMPO (2nd) |
2022-12-14 - 2022-12-16 |
Kagawa | Onsite (Sunport Takamatsu) and Online (Primary: On-site, Secondary: Online) |
A Consideration on Estimation Accuracy Improvement of Video Viewers' Emotions for Unlearned Data Using Bio-signals and Physical Features of Video Hiroki Ono, Ryuichi Inoue (Waseda Univ.), Mutsumi Suganuma (Tama Univ.), Wataru Kameyama (Waseda Univ.) |
The authors have been conducting a research of video viewers’ emotion estimation by deep neural network using bio-signal... [more] | |
| RCC, ITS, WBS | 2022-12-14 11:30 |
Shiga | Ritsumeikan Univ. BKC (Primary: On-site, Secondary: Online) |
A fundamental study of a drone classification method applying CNN to range and Doppler images obtained by a millimeter-wave fast chirp MIMO radar Masashi Kurosaki, Kenshi Ogawa, Ryohei Nakamura, Hisaya Hadama (NDA) WBS2022-46 ITS2022-22 RCC2022-46 |
In this paper, we propose a method to classifying various drones from range profile and micro Doppler images of a drone ... [more] | WBS2022-46 ITS2022-22 RCC2022-46 pp.65-70 |
| SIS | 2022-12-05 14:50 |
Osaka | (Primary: On-site, Secondary: Online) |
A Method of Automatic Wall Detection from Room Images by Deep Neural Networks Shunya Shimegi, Kaoru Arakawa (Meiji Univ.) SIS2022-27 |
In order to design interior renovation easily，a method of automatic wall area detection is proposed using deep neural ne... [more] | SIS2022-27 pp.21-25 |
| SIS | 2022-12-05 15:10 |
Osaka | (Primary: On-site, Secondary: Online) |
Application of Adversarial Training in Detection of Calcification Regions from Dental Panoramic Radiographs Sei Takano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2022-28 |
Calcification regions that are a sign of vascular diseases may be observed on dental panoramic radiographs. The finding ... [more] | SIS2022-28 pp.26-31 |
| DC, SS | 2022-10-25 14:40 |
Fukushima | (Primary: On-site, Secondary: Online) |
Comparison of the Coverage Indicators of Evaluation Data for the Convolutional Neural Networks Yuto Yokoyama, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakazima (NII) SS2022-27 DC2022-33 |
Neuron Coverage (NC) was proposed as a measure to quantify the usefulness of evaluation data against Deep Neural Network... [more] | SS2022-27 DC2022-33 pp.29-34 |
| OPE, OCS, LQE | 2022-10-20 17:10 |
Ehime | (Primary: On-site, Secondary: Online) |
Structural Design by Deep Learning for Improving Coupling Efficiency between Si Thin Wire and Topological Waveguide Itsuki Sakamoto, Tomohiro Amemiya, Sho Okada, Hibiki Kagami, Nobuhiko Nishiyama (Tokyo Tech), Xiao Hu (NIMS) OCS2022-25 OPE2022-71 LQE2022-34 |
We propose a structure design method using deep learning to achieve highly efficient coupling between a normal Si wavegu... [more] | OCS2022-25 OPE2022-71 LQE2022-34 pp.45-50 |
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