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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| 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 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 |
| US | 2022-09-20 13:50 |
Online | Online | Basic study of optimal training data creation conditions for computer-aided diagnosis using ultrasound images of breast tumors Makoto Yamakawa (SIT), Miho Kanda, Moe Ohshima (Kyoto Univ.), Takeshi Namita, Tsuyoshi Shiina (SIT) US2022-39 |
The quality of training data is important in the development of computer-aided diagnosis (CAD) that automatically detect... [more] | US2022-39 pp.10-13 |
| MI | 2022-09-15 10:00 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Automatic Multi-Measure Classification of Hip Osteoarthritis Based on Digitally-Reconstructed Radiographs using Deep Learning Masachika Masuda, Mazen Soufi, Yoshito Otake (NAIST), Keisuke Uemura (Osaka Univ.), Masaki Takao (Ehime Univ.), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2022-49 |
Hip Osteoarthritis (HOA) is usually diagnosed by radiographs. In addition to the degree of cartilage degeneration, the d... [more] | MI2022-49 pp.1-4 |
| CCS, NLP | 2022-06-09 15:20 |
Osaka | (Primary: On-site, Secondary: Online) |
Speeding-up by Reduction of Processing Paths in Octave Convolution Akito Yoshikawa, Hidehiro Nakano (Tokyo City Univ.) NLP2022-6 CCS2022-6 |
Octave Convolution (OctConv), one of the convolutional neural network methods, can also improve accuracy while reducing ... [more] | NLP2022-6 CCS2022-6 pp.27-30 |
| CCS, NLP | 2022-06-09 17:15 |
Osaka | (Primary: On-site, Secondary: Online) |
Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data Kaito Arase, Zhijian Wu, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2022-10 CCS2022-10 |
The authors have recently conducted training of Convolutional Neural Networks (CNNs) on OpenStreetMap images each of whi... [more] | NLP2022-10 CCS2022-10 pp.46-51 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 10:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
ECG waveform reconstruction method using FMCW radar sensing and transformer model Ren Anzai, Daiki Toda, Koichi Ichige (Yokohama National Univ.) SIP2022-4 BioX2022-4 IE2022-4 MI2022-4 |
In this paper, we propose a waveform reconstruction method for ECG (ElectroCardioGram) signals using a transformer, a ki... [more] | SIP2022-4 BioX2022-4 IE2022-4 MI2022-4 pp.19-24 |
| 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 |
| EA | 2022-05-13 16:25 |
Online | Online | Study on noise reduction with a single noisy speech based on Double-DIP Hien Oonaka (NITTC), Takuya Fujimura (Nagoya Univ.), Ryoichi Miyazaki (NITTC) EA2022-12 |
This paper proposes a new noise reduction method with an untrained Deep Neural Network ( DNN) for a single noisy speech.... [more] | EA2022-12 pp.54-61 |
| 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 | 2022-03-08 13:05 |
Online | Online | [Invited Talk]
--- Takashi Matsubara (Osaka Univ.) IBISML2021-34 |
Deep learning is being considered as the most promising approach to building an artificial intelligence (AI) system; it ... [more] | IBISML2021-34 p.27 |
| IBISML | 2022-03-09 10:15 |
Online | Online | [Invited Talk]
--- Takahiro Tsukahara (Tokyo University of Science) IBISML2021-41 |
Turbulence of viscoelastic fluids, such as dilute polymer/surfactant solutions, is of practical importance, because it c... [more] | IBISML2021-41 p.34 |
| MBE, NC (Joint) |
2022-03-03 09:45 |
Online | Online | Estimating the permeability of rocks using three-dimensional CNN and ResNet Taro Kamano (Kyushu Univ.), Yutaka Jitsumatsu (Tokyo Tech), Takeshi Tsuji (Kyushu Univ.) NC2021-60 |
Investigating the permeability and elastic wave velocity of rocks covering the surface of the earth is central in petrol... [more] | NC2021-60 pp.74-79 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 11:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
Study of Method for Improving Speech Intelligibility in Glossectomy Patients by Knowledge Distillation via Lip Features Kazushi Takashima, Masanobu Abe, Sunao Hara (Okayama Univ.) EA2021-81 SIP2021-108 SP2021-66 |
In this paper, we propose a voice conversion method for improving speech intelligibility uttered by glossectomy patients... [more] | EA2021-81 SIP2021-108 SP2021-66 pp.108-113 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 14:40 |
Online | Online | A Study on Object Detection in Omnidirectional Images Using Deep Learning Yasuyuki Ishida, Toshio Ito (SIT) ITS2021-57 IE2021-66 |
A minimum sensor configuration is desired for a popular automatic vehicle. In this study, an omnidirectional camera with... [more] | ITS2021-57 IE2021-66 pp.190-195 |
| IE | 2022-01-24 13:05 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee Maodong Xiang, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-31 |
MRI has a relatively long acquisition time, leading to patient comfort problems and artifacts from patient motion. Accel... [more] | IE2021-31 pp.21-26 |
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