| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| 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 |
| BCT, IEEE-BT, HOKKAIDO |
2023-07-28
11:50 |
Hokkaido |
Sapporo Business Innovation Center
(Primary: On-site, Secondary: Online) |
An Evaluation of Neural Network Parameters for Decoding (8,4) and (16,8) Polar Codes
Reona Kumaki, Hiroshi Tsutsui, Takeo Ohgane (Hokkaido Univ.) |
Polar codes are one type of error correction codes.
When operated with a sufficiently long code length, polar codes ca... [more] |
BCT2023-61
pp.49-52 |
| ME |
2023-07-21
15:45 |
Online |
Online |
Preliminary study on recognition of upward stairs by use of CNN and multi channelization for visually impaired people
Yuto Narumi, Hotaka Takizawa, Akihisa Ohya (Univ. of Tsukuba), Makoto Kobayashi (Tsukuba Univ. of Technology), Mayumi Aoyagi (Aichi Univ. of Education) |
Smartphones are used by many visually impaired people, and the users will increase in the future. In this research, we p... [more] |
ME2023-66
pp.17-18 |
| IIEEJ, AIT |
2023-06-04
10:20 |
Tokyo |
TOKYO CITY UNIVERSITY
(Primary: On-site, Secondary: Online) |
A Study on Pattern Recognition of Upper Body Clothes Using MediaPipePose
Kouki Maeda, Youngha Chang, Nobuhiko Mukai (TCU) |
In recent years, there are many studies that focuses on human region extraction and attribute recognition in images and ... [more] |
AIT2023-136
pp.47-50 |
| IIEEJ, AIT |
2023-06-04
11:00 |
Tokyo |
TOKYO CITY UNIVERSITY
(Primary: On-site, Secondary: Online) |
A study of feature extraction methods for clusters in image classification using deep metric learning
-- Visualization of features using factor information common to clusters --
Haruya Tanaka, Chanjin Seo, Jun Ohya (Waseda Univ.), Hiroyuki Ogata (Seikei Univ.) |
In recent years, the running population has been increasing, and demand for coaching systems for amateur runners is expe... [more] |
AIT2023-138
pp.55-58 |
| AIT, IIEEJ, AS, CG-ARTS |
2023-03-06
10:25 |
Tokyo |
Tokyo Polytechnic Univ. (Nakano)
(Primary: On-site, Secondary: Online) |
Using Deep Learning to Generate Wall Textures
ekitou chin, Masaki Abe, Taichi Watanabe (TUT) |
In recent years, the area of image processing and image generation using deep learning has been further developed and ap... [more] |
AIT2023-83
pp.175-176 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21
15:30 |
Hokkaido |
Hokkaido Univ. |
Evaluating The Effectiveness of Data Augmentation for Learning TrackNetV2
Yushan Wang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Data augmentation has been widely used in a variety of deep learning tasks, mostly with a positive impact on the results... [more] |
MMS2023-16 ME2023-36 AIT2023-16
pp.81-84 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [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] |
MMS2023-17 ME2023-37 AIT2023-17
pp.85-88 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-22
14:00 |
Hokkaido |
Hokkaido Univ. |
Hierarchical Minimum-Sized Object Detection Method using Clustering Algorithm for UAV Autonomous Flight
Yusei Horikawa, Makoto Sugaya, Tetsuya Matsumura (Nihon Univ) |
This paper describes an efficient minimum-sized object detection method in high-Resolution images for UAV autonomous fli... [more] |
MMS2023-31 ME2023-51 AIT2023-31
pp.235-238 |
| ME, SIP, TOKAI |
2022-12-07
10:50 |
Aichi |
(Primary: On-site, Secondary: Online) |
[Short Paper]
3D Facial Recognition for Genetic Studies based on PointNet++
Kazuma Okada, Takuma Terada, Jiaqing Liu (Ritsumeikan Univ.), Tomoko Tateyama (Fujita Health University), Ryosuke Kimura (Ryukyu Univ.), Yen Wei Chen (Ritsumeikan Univ.) |
Recently, the development of genetic research has found a relationship between human face shape and genes. By analyzing ... [more] |
ME2022-88 SIP2022-7
pp.9-11 |
| ME, SIP, TOKAI |
2022-12-07
13:35 |
Aichi |
(Primary: On-site, Secondary: Online) |
Skeleton Estimation for Swing based on Deep Model by Introducing Articulation Constraints into Training
Atsuki Sakata, Shogo Kihira, Nobutaka Shimada (Ritsumeikan Univ.), Yuki Nagano, Masahiki Ueda (SRI) |
For the purpose of developing the automatic diagnosis system of players' golf swing, we propose a method for estimating ... [more] |
ME2022-91 SIP2022-10
pp.23-26 |
| ME, SIP, TOKAI |
2022-12-07
15:00 |
Aichi |
(Primary: On-site, Secondary: Online) |
Comparison of gazing points and gaze prediction of midfielders in soccer training
-- for professional and college student soccer players --
Ryo Isa, Kyosuke Horio, Tsubasa Hirakawa, Takayoshi yamashita, Hironobu Fujiyoshi (Chubu Univ.) |
Gaze behavior in sports has received widespread attention. The purpose is to quantitatively analyze and compare the eye ... [more] |
ME2022-94 SIP2022-13
pp.35-38 |
| IIEEJ, AIT |
2022-10-30
14:50 |
Online |
on line |
[Invited Talk]
Advances in Image Editing Techniques with Deep Learning
Satoshi Iizuka (Univ. of Tsukuba) |
In this presentation, I will introduce how image editing techniques have been developed through deep learning. The metho... [more] |
AIT2022-175
p.13 |
| IIEEJ, AIT |
2022-10-30
16:00 |
Online |
on line |
A Trial of Recognition of Electronic Parts by Deep-Learning for Efficient Recycling
Yihong Tang, Tomonori Izumi (Ritsumeikan Univ.) |
In order to improve material-recycling of disposed electronic appliances, we aim to develop a system to analyze and cate... [more] |
AIT2022-177
pp.19-22 |
| IIEEJ, AIT |
2022-10-31
11:00 |
Online |
on line |
|
Along with the increase in the feeding rate of the pets such as dogs and cats, the amount of time people spend together ... [more] |
AIT2022-183
pp.45-48 |
| ME |
2022-07-22
16:50 |
Online |
Online |
Deep Learning based Compressed Sensing Image Reconstruction Using Multiple Signal Under-sampling Pattern
Masaki Shibui, Kazuki Yamato, Satoshi Ito (Utsunomiya Univ.) |
CS-MRI, which is an application of compressed sensing (CS) to MR image acquisition, can reconstruct MR images from a sma... [more] |
ME2022-71
pp.27-29 |
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-19
16:10 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
[Invited Talk]
Image and Video Restoration with Deep Learning
Satoshi Iizuka (Univ. of Tsukuba) |
In this talk, I will introduce techniques for restoring black-and-white images and videos with high accuracy using deep ... [more] |
|
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-20
09:40 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
User identification on Walking by Deep Learning Based on 3-axis Acceleration after Coordinate Transformation of Smartphone
Pengfei Yang, Qun Yang, Yuji Watanabe (Nagoya City Univ.) |
In this paper, we first transform the 3-axis acceleration data of smart phone, which is left in our laboratory, from the... [more] |
|