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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EMM, BioX, ISEC, SITE, ICSS, HWS, IPSJ-CSEC, IPSJ-SPT [detail] | 2023-07-25 09:00 |
Hokkaido | Hokkaido Jichiro Kaikan | CNN-Based Iris Recognition Using Multi-spectral Iris Images Ryosuke Kuroda, Tetsuya Honda, Hironobu Takano (Toyama Prefectural Univ.)  ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 |
Iris recognition using a near-infrared camera is generally known as a biometric authentication method with high accuracy... [more] | ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 pp.147-151 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 15:45 |
Hokkaido | Hokkaido Univ. | A Residual U-Net Architecture for Shuttlecock Detection Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Detection of fast-moving shuttlecocks is essential for badminton video analysis. Several methods based on deep learning ... [more] | |
| 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 |
| IBISML | 2022-01-17 10:40 |
Online | Online | Automatic Makeup Transfer with GANs and Its Quantitative Evaluation Cuilin Wang, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2021-20 |
Transferring makeup from a reference image with makeup to a source image without makeup has a wide range of application ... [more] | IBISML2021-20 pp.17-22 |
| IMQ | 2021-10-22 13:45 |
Osaka | Osaka Univ. | A Tiny Convolutional Neural Network for Image Super-Resolution Kazuya Urazoe, Nobutaka Kuroki, Yu Kato, Shinya Ohtani (Kobe Univ.), Tetsuya Hirose (Osaka Univ.), Masahiro Numa (Kobe Univ.) IMQ2021-7 |
This paper surveys three techniques for reducing computational costs of convolutional neural network (CNN) for image sup... [more] | IMQ2021-7 pp.2-7 |
| 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 |
| MI | 2019-01-23 14:00 |
Okinawa | Segmentation for diffuse lung disease opacities on CT images using U-Net and residual U-Net Kanako Murakami, Shoji Kido, Yasushi Hirano, Shingo Mabu (Yamaguchi Univ.), Kenji Kondo (AIST/Panasonic), Jun Ozawa (AIST) MI2018-102 |
Segmentation is important for diagnosis of diffuse lung diseases (DLD) as same as classification. In recent years, a lot... [more] | MI2018-102 pp.175-179 |
|
| RCS, AP (Joint) |
2018-11-22 11:00 |
Okinawa | Okinawa Industry Support Center | A simple evaluation method of residual aberration in reflect arrays Aoi Kotoura, Shigeru Makino, Yoshimi Sunaga (KIT), Michio Takikawa, Hiromasa Nakajima (Mitsubishi Electric) AP2018-132 |
In the conventional method of evaluating the phase error frequency characteristic, the relationship between the phase er... [more] | AP2018-132 pp.171-175 |
| IE | 2018-06-29 10:20 |
Okinawa | Single-image Rain Removal Using Residual Deep Learning Takuro Matsui, Masaaki Ikehara, Takanori Fujisawa (Keio Univ.) IE2018-23 |
Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal prob... [more] | IE2018-23 pp.13-18 |
|
| SANE | 2017-08-24 13:50 |
Osaka | OIT UMEDA Campus | Deep Learning for Target Classification from SAR Imagery
-- Data Augmentation and Translation Invariance -- Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30 |
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (... [more] | SANE2017-30 pp.13-17 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2015-09-15 14:30 |
Ehime | Image-based Face Modeling from Few Images Using Augmented Eigenface Hisayoshi Chugan, Tsuyoshi Fukuda, Takeshi Shakunaga (Okayama Univ.) PRMU2015-87 IBISML2015-47 |
A real-time face tracking and recognition system was proposed by Oka and Shakunaga [1], [2]. In their system, 24 images ... [more] | PRMU2015-87 IBISML2015-47 pp.143-148 |
|
| PRMU, MVE, IPSJ-CVIM [detail] | 2011-01-21 15:45 |
Shiga | High Frequency Compensated Face Hallucination Method So Sasatani, Xian-Hua Han, Motonori Ohashi, Yutaro Iwamoto, Yen-Wei Chen (Ritsumeikan Univ.) PRMU2010-189 MVE2010-114 |
Face Hallucination method is one of learning-based super-resolution techniques, which can reconstruct a high-resolution ... [more] | PRMU2010-189 MVE2010-114 pp.323-328 |
|
| EID, ITE-IDY | 2009-07-23 15:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Analysis of Impurity Ion Affecting Image Sticking Effect on Liquid Crystal Display Masanobu Mizusaki, Tetsuya Miyashita, Tatsuo Uchida (Tohoku Univ.), Yuichiro Yamada (Sharp. Corp.) EID2009-15 |
Image sticking can be observed in some case and it degrades the image quality of liquid crystal (LC) display. The image... [more] | EID2009-15 pp.13-16 |
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