| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| IST |
2025-03-21
11:05 |
Tokyo |
Kikai-Shinko-Kaikan Bldg.
(Primary: On-site, Secondary: Online) |
Lightweight Object Detection Model for a CMOS Image Sensor with Binary Feature Extraction
Keiichiro Kuroda, Yudai Morikaku, Yu Osuka, Ryoya Ieagaki, Kota Yoshida, Shunsuke Okura (Ritumeikan Univ) |
For the coming of IoT age, we have proposed an object detection system using a CMOS image sensor with binary feature ext... [more] |
IST2025-11
pp.7-8 |
| ME, AIT, MMS, IEICE-IE, IEICE-ITS, SIP [detail] |
2025-02-18
12:50 |
Hokkaido |
Hokkaido Univ. |
A Note on Interpretability of Visual Language Model by Few-shot Learning based on the Linear Representation Hypothesis
Hiroki Okamura, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Visual language models (VLMs), pre-trained on vast amounts of web-based images and text, have demonstrated impressive ze... [more] |
MMS2025-7 ME2025-7 AIT2025-7 SIP2025-7
pp.34-39 |
| ME, AIT, MMS, IEICE-IE, IEICE-ITS, SIP [detail] |
2025-02-19
13:10 |
Hokkaido |
Hokkaido Univ. |
Experiments on image recognition by optoelectronic deep neural network with scattering medium insertion
Kaito Inoue, Takumi Hashiguchi, Taichi Takatsu, Rio Tomioka (Kyutech), Atsushi Shibukawa (Hokkaido Univ.), Masanori Takabayashi (Kyutech) |
Optoelectronic deep neural network (OE-DNN) has been proposed as a solution to address the increasing energy consumption... [more] |
MMS2025-33 ME2025-33 AIT2025-33 SIP2025-33
pp.161-166 |
| SIP |
2024-03-21
15:20 |
Ibaraki |
Center for Computational Sciences, University of Tsukuba |
A Study on Fast Extraction Method of All Pitches from Baseball Game Video
Shohei Adachi, Hiroto Fukuta, Hiroshi Watanabe (Waseda Univ.) |
In TV sports and news programs, there is a demand for extracting and broadcasting all pitching scene as a highlight afte... [more] |
SIP2024-6
pp.16-19 |
| ME |
2024-02-10
09:45 |
Online |
online |
A Study of Work Time Reduction Methods for Individual Recognition in Zoo
Kanon Shimodaira, Ayana Rikimaru (NIT,NC) |
This study investigated the feasibility of a real-time recognition system with the goal of improving the experience of o... [more] |
ME2024-1
pp.1-3 |
| ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN, IPSJ-AVM [detail] |
2023-09-07
09:50 |
Osaka |
Osaka Metropolitan Univ.
(Primary: On-site, Secondary: Online) |
Improving Performance of Convolutional Neural Network-Based Driver Behavior Recognition
Shengbiao Wang, Koji Iwano (Tokyo City Univ.) |
This study investigates the automatic recognition of driver behaviors using images captured by in-vehicle cameras for th... [more] |
ME2023-90
pp.7-12 |
| AIT, IIEEJ, AS, CG-ARTS |
2023-03-06
14:38 |
Tokyo |
Tokyo Polytechnic Univ. (Nakano)
(Primary: On-site, Secondary: Online) |
Mouse classification system used by players in the e-Sports scene.
Takuma Sugiura, Kazuya Ueki (Meisei Univ.) |
In recent years, e-Sports, in which computer games are regarded as sports competitions, has become increasingly popular.... [more] |
AIT2023-65
pp.111-114 |
| ME |
2022-07-22
16:20 |
Online |
Online |
Image Classification of Cancer Cells Treated with IRDAptermer
Rahman Rawnak Mim, Yuuka Yamagata, Yoshiro Chuman, Shogo Muramatsu (Niigata Univ.) |
This work evaluates convolutional neural network (CNN)-based image classifiers for determining the effect of a cell memb... [more] |
ME2022-70
pp.23-26 |
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-20
16:20 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis
Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) |
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] |
|
| AIT, IIEEJ, AS, CG-ARTS |
2022-03-08
09:53 |
Online |
Online |
Manga Inpainting with Author Classification and Line Drawing Clues
Hiroyuki Yokota (TUS), Ryosuke Furuta (UTokyo), Yukinobu Taniguchi (TUS), Ryota Hinami, Shonosuke Ishiwatari (Mantra) |
In order to translate Japanese manga into another language, it is necessary to recognize the contents and position of te... [more] |
AIT2022-51
pp.53-56 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [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] |
MMS2022-8 ME2022-33 AIT2022-8
pp.43-48 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [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) |
During domain incremental learning of image classification task, the distribution of images continually change, and mode... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
10:15 |
Online |
online |
Classification of User's Device Possession Position and Behavior by Using Deep Metric Learning
Rui Kitahara, Lifeng Zhang (Kyutech) |
With the widespread use of smartphones, there have been efforts to classify human behavior using built-in sensors. Howev... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
11:10 |
Online |
online |
Enhancing Personalized Food Image Classifier by Visual Attention and Class-Dependent Weighting
Seum Kim, Yoko Yamakata, Kiyoharu Aizawa (UTokyo) |
In a real-world setting, food records are very noisy and strongly imbalanced. Besides, inter-class similarity and intra-... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
16:45 |
Online |
online |
A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a note on accurate distress classification using deep learning considering confidence in attention m... [more] |
MMS2022-33 ME2022-58 AIT2022-33
pp.371-376 |
| BCT, IEICE-SIS |
2021-10-07
14:25 |
Online |
online |
Block-wise Transformation with Secret Key for Adversary Robust Defence of SVM model
Ryota Iijima, MaungMaung AprilPyone, Hitoshi Kiya (TMU) |
In this paper, we propose a method for implementing support vector machine (SVM) models that are robust against adversar... [more] |
|
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
11:35 |
Online |
Online |
A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level... [more] |
MMS2021-4 ME2021-4 AIT2021-4
pp.17-21 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
15:40 |
Online |
Online |
Texture Analysis and Evaluation of the Shitsukan Research Database Based on Luminance Information
Norifumi Kawabata (Tokyo Univ. of Science) |
One of texture component on objects and graphics is luminance. By strength of luminance, human visual perception for cla... [more] |
MMS2021-11 ME2021-11 AIT2021-11
pp.53-58 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-19
14:05 |
Online |
Online |
[Special Talk]
A Note on Discrimination of Road Surface Conditions Based on Deep Learning Using Road Images
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study the discrimination of road surface conditions based on deep learning using images captured by fi... [more] |
MMS2021-21 ME2021-21 AIT2021-21
pp.165-169 |
| SIP, ME |
2019-11-14
11:25 |
Kumamoto |
Sojo University, Main Campus |
Trial of three-dimensional extraction and classification of cell nucleus region in heart
Asuma Takematsu, Masahiro Migita, Masashi Toda, Yuichiro Arima (Kumamoto Univ.) |
In this study, after extracting the region of the cell nucleus from the cell nucleus image, I focused on only the typica... [more] |
ME2019-116
pp.9-12 |