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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CPSY, IPSJ-ARC | 2023-01-10 13:00 |
Online | Online | An Efficient Multi-Head Self-Attention using Neural ODE for FPGAs Ikumi Okubo, Keisuke Sugiura, Hiroki Kawakami, Hiroki Matsutani (Keio Univ.)  CPSY2022-27 |
(To be available after the conference date) [more] | CPSY2022-27 pp.1-6 |
| MSS, SS | 2023-01-11 15:50 |
Osaka | (Primary: On-site, Secondary: Online) |
Improvement of Composite SVM in HSI Classification Tamura Akito, Kitamura Takuya (NIT) MSS2022-61 SS2022-46 |
In this paper, we propose an improved method of composite support vector machines for hyper-spectral image classificatio... [more] | MSS2022-61 SS2022-46 pp.96-100 |
| IBISML | 2022-12-22 15:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
[Short Paper]
Semi supervised image classification using unreliable pseudo label Jihong Hu, Yinhao Li, Yen-Wei Chen (Ritsumeikan Univ.) IBISML2022-47 |
Semi-supervised learning (SSL), which automatically annotates unlabeled data with pseudo labels during training, has ach... [more] | IBISML2022-47 pp.24-29 |
| SANE | 2022-12-16 10:45 |
Nagasaki | Nagasaki Public Hall (Primary: On-site, Secondary: Online) |
Machine learning for global detection of photovoltaic panel installation using Landsat-8 Imagery Ryo Ito, Ryu Sugimoto, Chiaki Tsutsumi, Ryosuke Nakamura (AIST) SANE2022-77 |
Nowadays, free-and-open medium-resolution satellite imagery can be utilized for sustainable monitoring of global changes... [more] | SANE2022-77 pp.74-76 |
| PRMU | 2022-12-16 14:10 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Sampling Strategies in Data Pruning Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2022-48 |
Data Pruning is a method of selecting the training data out of an entire training dataset so as to keep the accuracy aft... [more] | PRMU2022-48 pp.85-90 |
| PRMU | 2022-12-16 14:40 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Data Augmentation Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.) PRMU2022-50 |
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of i... [more] | PRMU2022-50 pp.95-99 |
| MBE, NC | 2022-12-03 11:00 |
Osaka | Osaka Electro-Communication University | Image Classification Using Gabor Filters as Preprocessing for CNNs Akito Morita, Hirotsugu Okuno (OIT) MBE2022-32 NC2022-54 |
Image preprocessing is a promising approach to improve accuracy in image classification using convolutional neural netwo... [more] | MBE2022-32 NC2022-54 pp.43-46 |
| MICT, MI | 2022-11-18 13:00 |
Aichi | Nagoya Institute of Technology | [Invited Talk]
Transition of medical image processing research towards Society 5.0 Kunihiro Chihara (Jikei Univ. of Health Care Sci.) MICT2022-37 MI2022-66 |
When the world began to shift to an information society, research into automatic diagnosis using computers began in the ... [more] | MICT2022-37 MI2022-66 pp.18-23 |
| MI | 2022-09-15 15:15 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Learning of Squamous Cell Image Classification Model Using Preference Learning to Assist Cervical Cytology Yuta Nambu (Future Univ. Hakodate), Tasuku Mariya, Syota Shinkai, Mina Umemoto, Hiroko Asanuma, Yoshihiko Hirohashi, Tsuyoshi Saito, Toshihiko Torigoe (Sapporo Medical Univ.), Ikuma Sato, Yuichi Fujino (Future Univ. Hakodate) MI2022-62 |
To support cervical cell diagnosis, Various classification methods of cervical cell images using machine learning have b... [more] | MI2022-62 pp.53-58 |
| PRMU | 2022-09-14 11:00 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Disease and severity classification of coffee leaf images by global low-level feature aggregation network Takuhiro Okada, Satoshi Iizuka, Kazuhiro Fukui (Univ. of Tsukuba) PRMU2022-14 |
Coffee leaf disease is one of the most important problems in coffee production. It is very important in the coffee produ... [more] | PRMU2022-14 pp.25-30 |
| ICD, SDM, ITE-IST [detail] | 2022-08-10 16:00 |
Online | IC with Integrated Imager and Ultra-Low Latency All-Digital In-Imager 2D Binary Convolutional Neural Network Accelerator for Image Classification Ruizhi Wang, Cheng-Hsuan Wu, Makoto Takamiya (The Univ. of Tokyo) SDM2022-53 ICD2022-21 |
In the field of real-time image recognition, the computing latency of convolutional neural network become an issue. In t... [more] | SDM2022-53 ICD2022-21 pp.87-92 |
|
| IN, CCS (Joint) |
2022-08-04 10:20 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Consideration of the structure of CNN models with high image classification performanc Mizuki Dai, Kenya Jinno (Tokyo City University) CCS2022-27 |
In recent years, Transformer-based models such as ViT have shown remarkable performance in image recognition using Deep ... [more] | CCS2022-27 pp.6-9 |
| CAS, SIP, VLD, MSS | 2022-06-16 15:05 |
Aomori | Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Image Classification Using Vision Transformer for Compressible Encrypted Images Genki Hamano, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (Tokyo Metropolitan Univ.) CAS2022-8 VLD2022-8 SIP2022-39 MSS2022-8 |
In this paper, we propose an image classification method for compressible encrypted images without losing classification... [more] | CAS2022-8 VLD2022-8 SIP2022-39 MSS2022-8 pp.40-45 |
| IMQ | 2022-05-27 14:25 |
Tokyo | Classification-ESRGAN
-- Synthesis of super-resolution images based on subject categorization -- Jingan Liu, Atsumu Harada, Naiwala P. Chandrasiri (Kogakuin Univ.) IMQ2022-3 |
In recent years, super-resolution techniques have been significantly developed based on deep learning. In particular, GA... [more] | IMQ2022-3 pp.12-17 |
|
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-20 16:20 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 |
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] | SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 pp.144-149 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 10:55 |
Online | Online (Zoom) | Phalange bone classification and osteotomy line estimation from X-ray images using deep learning Tatsuhiko Sotokawa, Takaharu Yamazaki (SIT), Kazuaki Tanaka (NEOMED.), Keizo Fukumoto (Saitama Jikei Hosp.) IMQ2021-10 IE2021-72 MVE2021-39 |
In this study, we detect and classify the phalanges and estimate the osteotomy line from hand X-ray images using deep le... [more] | IMQ2021-10 IE2021-72 MVE2021-39 pp.1-6 |
| NLC | 2022-03-07 11:05 |
Online | Online | Sightseeing spot detection and automatic generation of explanations using Flickr Hanami Ookada (Hiroshima City Univ.), Aya Ishino (HUE), Toshiyuki Takezawa (Hiroshima City Univ.) NLC2021-30 |
Using travel photos with latitude and longitude information in Flickr and image recognition technology, we conducted a s... [more] | NLC2021-30 pp.13-18 |
| MBE, NC (Joint) |
2022-03-02 09:30 |
Online | Online | A Study on Improvement of Recognition Accuracy and Speed-up of SOM-based Classification System Shun Tasaka, Hiroomi Hikawa (Kansai Univ.) NC2021-46 |
This paper discusses a new type of image classifier called class-SOM, which is based on self-organizing map (SOM). The... [more] |
NC2021-46 pp.1-4 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:15 |
Online | Online | A Note on Improvement of Accuracy in Classification of Distress Images for Efficient Inspection of Road Structures
-- Introduction of Ratio of Similar Cases Based on Text Data -- Taisei Hirakawa, Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we propose a method for correcting the results of distress image classification using text data recorded ... [more] | |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:00 |
Online | Online | Domain Incremental Leaning with Adaptive Loss Functions Takumi Kawashima (UTokyo), Go Irie, Daiki Ikami (NTT), Kiyoharu Aizawa (UTokyo) ITS2021-30 IE2021-39 |
During domain incremental learning of image classification task, the distribution of images continually change, and mode... [more] | ITS2021-30 IE2021-39 pp.31-36 |
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