| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| AIT, IIEEJ, AS, CG-ARTS |
2025-03-10
13:37 |
Tokyo |
Tokyo Polytechnic Univ. (Nakano) |
An Experimental Study on 3D Hair Modeling from Sketches Using Diffusion Curves
Ritsuki Ishiwata (Hosei Univ), Syuhei Sato (Hosei Univ/PCGR) |
In this study, we propose a method for generating 3D hair models from sketches. One approach to 3D modeling from a singl... [more] |
AIT2025-67
pp.102-105 |
| AIT, IIEEJ, AS, CG-ARTS |
2025-03-10
14:25 |
Tokyo |
Tokyo Polytechnic Univ. (Nakano) |
SVBRDF Prediction based on Two-Level Basis from Multiple Input Images
Tomoya Kozuki, Kei Iwasaki (Saitama Univ.) |
This paper proposes a model that predicts Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) usi... [more] |
AIT2025-70
pp.114-117 |
| BCT, IEEE-BT |
2025-03-07
13:30 |
Okinawa |
(Primary: On-site, Secondary: Online) |
[Invited Talk]
How AI Technology Can Empower Broadcast Media
Toru Imai (NHK) |
Since the term ‘Artificial Intelligence’ was coined 70 years ago, AI technology has developed remarkably. In recent year... [more] |
BCT2025-47
pp.18-21 |
| ME, AIT, MMS, IEICE-IE, IEICE-ITS, SIP [detail] |
2025-02-18
11:25 |
Hokkaido |
Hokkaido Univ. |
Efficient Physics Informed Dynamic Neural Fluid Fields Reconstruction From Sparse Video
Yangcheng Xiang, Yoshinori Dobashi (Hokudai) |
Efficiently inferring the latent physical properties of fluids from sparse 2D videos has long been a challenging problem... [more] |
MMS2025-6 ME2025-6 AIT2025-6 SIP2025-6
pp.29-33 |
| KANSAI |
2024-12-22
09:30 |
Osaka |
Osaka Metropolitan University, I-site Namba |
**
Youhei Ueno, Masataka Seo (Osaka Institute of Technology) |
There are two problems with video generation using deep generative models. The first problem is that objects moving betw... [more] |
|
| HI, VRPSY, JSKE |
2024-11-16
10:00 |
Osaka |
Kindai Univ. |
Deep learning mind-reading model based on gaze information
Nayuta Tada, Takeshi Kohama (Kindai Univ.) |
In this study, intending to establish a mind-reading technique based on gaze information, we developed a model that exte... [more] |
HI2024-47
pp.47-50 |
| IEICE-SIS, BCT |
2024-10-03
14:50 |
Hokkaido |
Hokusei Gakuen Univ.
(Primary: On-site, Secondary: Online) |
Accuracy Improvement of Real Image Classification Using Synthetic Images for Training by Bilateral Filtering
Masakazu Ohkoba, Takeru Inoue, Miho Adachi, Junya Morioka (Meiji Univ.), Kouji Gakuta, Etsuji Yamada, Aoi Kariya (DPS), Masakazu Kinosada, Yujiro Kitaide (Shinsei Printing Co., Ltd.), Ryusuke Miyamoto (Meiji Univ.) |
In the development of machine-learning-based component classification applications, creating a suitable training dataset... [more] |
|
| IEICE-SIS, BCT |
2024-10-03
15:10 |
Hokkaido |
Hokusei Gakuen Univ.
(Primary: On-site, Secondary: Online) |
A New Calcification Region Detector for Dental Panoramic Radiographs
Sota Nakano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Nanae Dewake, Nobuo Yoshinari (Matsumoto Dental Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) |
Calcification regions are sometimes observed in carotid arteries on dental panoramic radiographs and are expected to be ... [more] |
|
| ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN, IPSJ-AVM [detail] |
2024-09-04
15:30 |
Hiroshima |
Hiroshima Institute of Technology
(Primary: On-site, Secondary: Online) |
The Research on Dangerous Behavior Recognition and Warning System for Escalators Based on Deep Learning Models
YongXuan Zhu, Hiroyuki Nakamura (S.I.T) |
In this study, we developed a system that uses deep learning and image recognition technology to identify potential dang... [more] |
ME2024-80
pp.1-5 |
| ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN, IPSJ-AVM [detail] |
2024-09-04
16:10 |
Hiroshima |
Hiroshima Institute of Technology
(Primary: On-site, Secondary: Online) |
Restoring Missing Regions in "Shihai-monjyo" by Applying Image Inpainting Method
Haruto Izumi, Masahiro Migita, Masashi Toda, Masahiko Itoh (Kumamoto Univ.) |
Because paper was precious in those days, some ancient documents were written on both the front and back sides of the do... [more] |
ME2024-82
pp.11-16 |
| ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN, IPSJ-AVM [detail] |
2024-09-05
16:30 |
Hiroshima |
Hiroshima Institute of Technology
(Primary: On-site, Secondary: Online) |
Proposal of an Emotion Recognition System for Improving Video Viewing Experience of Visually Impaired Individuals
Zhiyuan Ning, Hiroyuki Nakamura (S.I.T) |
The rapid growth of short video platforms like TikTok has highlighted the need for improved accessibility for visually i... [more] |
ME2024-86
pp.37-40 |
| OSJ-HODIC, AIT, 3DMT, IDY, IEICE-EID, IEE-OQD, SID-JC |
2024-09-02
14:30 |
Tokyo |
Kikai-Shinko-Kaikan Bldg
(Primary: On-site, Secondary: Online) |
[Invited Talk]
Deep Learning in Projection Mapping
Daisuke Iwai (UOsaka) |
Projection mapping (PM) allows users to experience virtual and augmented reality without wearing displays by projecting ... [more] |
IDY2024-39 AIT2024-161 3DMT2024-50
pp.36-39 |
| ME, IST, IEICE-BioX, IEICE-SIP, IEICE-MI, IEICE-IE [detail] |
2024-06-07
13:15 |
Niigata |
Nigata University (Ekinan-Campus "TOKIMATE") |
Color information restoration from printed and scanned grayscale images with deep learning
Takehiro Muroya, Hiroshi Higashi, Yuichi Tanaka (OU) |
In this report, we propose a colorization method for gray-scale images embedded color information with the wavelet trans... [more] |
|
| ME, IST, IEICE-BioX, IEICE-SIP, IEICE-MI, IEICE-IE [detail] |
2024-06-07
13:40 |
Niigata |
Nigata University (Ekinan-Campus "TOKIMATE") |
Quality Control Method on H.264 for Data Size Reduction of Industrial Videos
Takahiro Naruko, Hiroaki Akutsu (Hitachi) |
In this study, for the goal of reducing data size of industrial videos, we propose quality control method on H.264 that ... [more] |
|
| SIP |
2024-03-21
14:25 |
Ibaraki |
Center for Computational Sciences, University of Tsukuba |
Bunch of Tricks for Improving Shuttlecock Detection from Badminton Videos
Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Accurate identification of the shuttlecock is necessary for video analysis in badminton matches, but, it remains difficu... [more] |
SIP2024-4
pp.8-11 |
| AIT, IIEEJ, AS, CG-ARTS |
2024-03-05
13:42 |
Tokyo |
Tokyo University of Technology |
Development of a Diagnostic Support System for Intranasal Disease
Kaho Ukai, Youngha Chang, Nobuhiko Mukai (TCU), Kojiro Hirano, Kouzou Murakami (SUSM/SUH) |
In this research, a support system has been developed to diagnose whether an endoscopic image shows "severely abnormal n... [more] |
AIT2024-70
pp.135-138 |
| AIT, IIEEJ, AS, CG-ARTS |
2024-03-05
15:04 |
Tokyo |
Tokyo University of Technology |
Brittle Fracture Shape Generation of Plane Objects by Conditional GAN
Yuma Aoki, Kohei Tokoi (Wakayama Univ.) |
Physics-based fracture simulation can generate realistic debris shapes, but it is difficult to use in applications that ... [more] |
AIT2024-114
pp.284-287 |
| 3DMT |
2023-10-02
13:05 |
Aichi |
(Primary: On-site, Secondary: Online) |
[Tutorial Invited Lecture]
From Mathematical Modeling to Data-Driven Optimization
-- Compressive Light Field Acquisition Undergoes Paradigm Shift --
Keita Takahashi (Nagoya Univ.) |
The light field is a basic representation for 3-D visual information, and it is usually treated as a set of images taken... [more] |
3DMT2023-35
p.1 |
| IST |
2023-09-15
11:45 |
Tokyo |
Kikai-Shinko-Kaikan Bldg.
(Primary: On-site, Secondary: Online) |
Pseudo-dToF using deep learning with time-compressive computational CMOS image sensor
Michitaka Yoshida (JSPS), Pham Ngoc Anh, Lioe De Xing, Keita Yasutomi, Shoji Kawahito, Keiichiro Kagawa (Shizuoka Univ.), Hajime Nagahara (Osaka Univ.) |
Depth imaging by the indirect ToF method has a problem in whitch measurement errors occur due to multiple reflections fr... [more] |
IST2023-37
pp.9-12 |
| AIT, 3DMT, OSJ-HODIC |
2023-09-08
17:15 |
Tokyo |
Nihon Univ. College of Science and Technology (Surugadai Campus) |
|
Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range (−π, π]. Conventi... [more] |
AIT2023-147 3DMT2023-34
pp.33-36 |