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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-03 17:18 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
3D shape reconstruction of colon with model-based unsupervised depth estimation Natsu Onozaka (Nagoya Univ.), Hayato Itoh (Fukuoka Univ.), Masahiro Oda (Nagoya Univ.), Masashi Misawa (Showa Univ.), Yuichi Mori (UiO), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.)  MI2023-60 |
We propose unsupervised trainig for the pose estimation in 3D reconstrcution of the colon from colonoscopic images by cl... [more] | MI2023-60 pp.87-90 |
| IA | 2023-09-22 10:40 |
Hokkaido | Hokkaido Univeristy (Primary: On-site, Secondary: Online) |
OLIViS: An OSINT-Based Lightweight Method for Identifying Video Content Services for Capacity Planning in Backbone ISPs Yuki Tamura, Fumio Teraoka, Takao Kondo (Keio Univ.) IA2023-23 |
As of 2022, 66% of Internet traffic is generated by video content services, among which Netflix and YouTube are the domi... [more] | IA2023-23 pp.75-82 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-02-28 17:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
DNN-based Noise Reduction Using Noise Signal for Target Signal Ryota Hiromasa, Hien Ohnaka, Ryoichi Miyazaki (NITTC) EA2022-93 SIP2022-137 SP2022-57 |
This study proposes a DNN-based noise reduction method that uses noise signals instead of clean speech signals as the ta... [more] | EA2022-93 SIP2022-137 SP2022-57 pp.101-106 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 11:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use Takuya Fujimura, Tomoki Toda (Nagoya Univ.) EA2022-112 SIP2022-156 SP2022-76 |
Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard... [more] | EA2022-112 SIP2022-156 SP2022-76 pp.221-226 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online | SP2022-13 |
We investigate the method for unsupervised learning of artifacts correction networks used for post-processing of Multi B... [more] | SP2022-13 pp.49-54 |
| MW | 2022-03-04 11:10 |
Online | Online | Deep-Learning Based Anomaly Detection Method for Microwave Non-destructive Road Monitoring Takahide Morooka, Shouhei Kidera (Univ. of Electro-Communications) MW2021-134 |
Microwave radar is promising as large-scale and speedy non-destructive monitoring tool for aging road or tunnel because ... [more] | MW2021-134 pp.128-133 |
| MI | 2022-01-26 13:00 |
Online | Online | Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59 |
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] | MI2021-59 pp.59-64 |
| MI | 2022-01-26 15:00 |
Online | Online | [Special Talk]
TBA Ryoma Bise (Kyushu Univ.) MI2021-66 |
Supervised learning (e.g., deep learning) has been used for various tasks in biomedical image analysis. While supervised... [more] | MI2021-66 p.88 |
| PRMU | 2021-12-16 11:00 |
Online | Online | Anomaly Detection using PatchCore with Self-attention module Yuki Takena (Shizuoka Univ.), Yoshiki Nota, Rinpei Mochizuki (Meidensya Corp.), Itaru Matsumura (Railway Technical Research Inst.), Gosuke Ohashi (Shizuoka Univ.) PRMU2021-29 |
In recent years, in visual inspection of industrial products using deep learning, There are many models that achieve exc... [more] | PRMU2021-29 pp.31-36 |
| PRMU | 2021-12-16 14:45 |
Online | Online | Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2021-31 |
Most of the existing deep learning based logo detection methods typically use a large amount of annotated training data,... [more] | PRMU2021-31 pp.43-44 |
| QIT (2nd) |
2021-11-30 13:30 |
Online | Online | [Poster Presentation]
Machine Learning techniques for unitary design classification:A comparative study Yaswitha Gujju, Bo Yang, Dr. Yuko Kuroki, Dr. Hiroshi Imai (UTokyo) |
The recent use of correlator functions to identify the degree of pseudorandomness in a qubit system opens up immense pos... [more] | |
| PRMU | 2021-08-26 10:00 |
Online | Online | Unsupervised non-rigid alignment for multiple noisy images Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.) PRMU2021-7 |
We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decompositio... [more] | PRMU2021-7 pp.1-6 |
| SIP | 2021-08-23 13:25 |
Online | Online | A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data Hiroki Narita, Akira Tamamori (AIT) SIP2021-28 |
In recent years, anomaly detection research in the field of computer vision has focused on methods based on transfer lea... [more] | SIP2021-28 pp.5-10 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 13:05 |
Online | Online | [Invited Talk]
* Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29 |
Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learne... [more] | EA2020-64 SIP2020-95 SP2020-29 pp.27-32 |
| PRMU | 2020-12-17 14:55 |
Online | Online | Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function
-- Application to automotive wire harness components -- Yuki Matsumoto (SEI) PRMU2020-45 |
Semantic segmentation, in which images are classified into pixel-by-pixel classes by deep learning, has been widely stud... [more] | PRMU2020-45 pp.42-46 |
| PRMU | 2020-12-17 15:10 |
Online | Online | Hierarchical Contrastive Adaptation for Cross-Domain Object Detection Ziwei Deng, Quan Kong, Naoto Akira, Tomoaki Yoshinaga (Hitachi) PRMU2020-46 |
Object detection based on deep learning has been enormously developed in recent years. However, applying detectors train... [more] | PRMU2020-46 pp.47-52 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2020-11-17 14:25 |
Online | Online | Energy-Efficient ECG Signals Outlier Detection Hardware Using a Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, HIroki Nakahara (Tokyo Tech) VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36 |
Advancements in portable electrocardiographs have allowed electrocardiogram (ECG) signals to be recorded in everyday lif... [more] | VLD2020-17 ICD2020-37 DC2020-37 RECONF2020-36 pp.36-41 |
| MI | 2020-01-29 10:05 |
Okinawa | OKINAWAKEN SEINENKAIKAN | A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets
-- Experimental evaluations of feature generation by small datasets -- Kazuyuki Ushifusa, Mitsutaka Nemoto(, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Atsuko Tanaka (Kindai Uni.), Naoto Hayashi (The Uni of Tokyo Hosp) MI2019-68 |
In a computer-aided detection system, image features are essential factors. In this study, we propose an image feature g... [more] | MI2019-68 pp.15-18 |
| HWS, VLD | 2019-02-28 13:55 |
Okinawa | Okinawa Ken Seinen Kaikan | Model Compression for ECG Signals Outlier Detection Hardware trained by Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, Hiroki Nakahara (Titech) VLD2018-114 HWS2018-77 |
In recent years, portable electrocardiographs and wearable devices have begun to spread so that electrocar- diogram (ECG... [more] | VLD2018-114 HWS2018-77 pp.127-132 |
| MI | 2019-01-23 14:00 |
Okinawa | Unsupervised Shadow Detection for Ultrasound Images by Deep Learning Suguru Yasutomi (FLL), Akira Sakai (FATEC), Masaaki Komatsu (Riken), Ryu Matsuoka, Reina Komatsu, Tatsuya Arakaki, Mayumi Tokunaka (Showa-U), Hidenori Machino, Kazuma Kobayashi (NCC), Ken Asada (Riken), Syuzo Kaneko (NCC), Akihiko Sekizawa (Showa-U), Ryuji Hamamoto (Riken) MI2018-96 |
Medical ultrasound is widely used for diagnosing internal organs since it is non-invasive. Shadows are often appear in u... [more] | MI2018-96 pp.151-156 |
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