| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| 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 |
| 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 |
| IST |
2024-11-08
13:40 |
Tokyo |
Morito Mem. Hall
(Primary: On-site, Secondary: Online) |
[Invited Talk]
Deep compressive sensing with coded image sensor
Michitaka Yoshida (JSPS), Daisuke Hayashi, Lioe De Xing, Keita Yasutomi, Shoji Kawahito, Keiichiro Kagawa (Shizuoka Univ.), Hajime Nagahara (Osaka Univ.) |
In this paper, we introduce a method of compressed sensing using coded CMOS sensors and the concept of deep sensing. By ... [more] |
IST2024-56
pp.20-24 |
| 3DMT |
2024-10-29
10:45 |
Tokyo |
(Primary: On-site, Secondary: Online) |
Compressing Phase-only Holograms via Phase Unwrapping
Yoshiki Watanabe, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii (NU) |
We propose a compression method for phase-only holograms. We first apply a phase unwrapping algorithm to the target phas... [more] |
3DMT2024-60
pp.9-12 |
| 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] |
|
| IEICE-CPM, IEICE-MRIS, IEICE-OME, MMS [detail] |
2023-10-26
13:00 |
Niigata |
(Primary: On-site, Secondary: Online) |
[Invited Talk]
Development of magneto-optical diffractive deep neural network device
Takayuki Ishibashi, Hotaka Sakaguchi, Jian Zhang, Fatima Chafi Zhara (Nagaoka Univ. Tech.), Hirofumi Nonaka (Aichi Inst. Tech.), Satoshi Sumi, Hiroyuki Awano (Toyota Tech. Inst.N) |
We propose a magneto-optical diffractive deep neural network (MO-D2NN). We simulated several MO-D2NNs, each of which con... [more] |
MMS2023-38
pp.1-2 |
| 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 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21
13:00 |
Hokkaido |
Hokkaido Univ. |
Fast designing method of additional patterns in self-referential holographic data storage
-- Approach using deep neural network --
Kazuki Chijiwa, Masanori Takabayashi (Kyushu Inst. of Tech.) |
In self-referential holographic data storage (SR-HDS) known as a purely one-beam holographic recording method, it has be... [more] |
MMS2023-7 ME2023-27 AIT2023-7
pp.35-40 |
| 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 |
| 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
16:40 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN
Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) |
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable co... [more] |
|
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-20
17:00 |
Kumamoto |
Kumamoto University
(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) |
(To be available after the conference date) [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
13:15 |
Online |
online |
Towards Universal Deep Image Compression
Koki Tsubota (UTokyo), Hiroaki Akutsu (Hitachi), Kiyoharu Aizawa (UTokyo) |
In this paper, we investigate deep image compression towards universal usage. In image compression, it is desirable to b... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
15:35 |
Online |
online |
Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT.
Yuqiao Yang, Muneyuki Sato, Ze Jin, Kenji Suzuki (Tokyo Tech) |
Based on a 3D massive-training artificial neural network (MTANN) combined with a Hessian-based ellipse enhancer, a small... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
14:40 |
Online |
online |
A Study on Object Detection in Omnidirectional Images Using Deep Learning
Yasuyuki Ishida, Toshio Ito (SIT) |
A minimum sensor configuration is desired for a popular automatic vehicle. In this study, an omnidirectional camera with... [more] |
|
| BCT, IEICE-SIS |
2021-10-08
10:00 |
Online |
online |
[Tutorial Lecture]
The Past and The Future of Explainable AI Techniques
Yoshitaka Kameya (Meijo Univ.) |
Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a cen... [more] |
|
| BCT, IEEE-BT |
2021-09-03
13:35 |
Online |
Online |
Convolutional Radio Modulation Recognition Networks with Attention Models in Wireless Systems
Haohui Jia, Na Chen, Minoru Okada (NAIST) |
In modern wireless systems, deep learning (DL) shows promising performance for wireless signal processing. DL model driv... [more] |
BCT2021-35
pp.1-4 |