| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| 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 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
14:50 |
Online |
Online |
A Note on Estimation of Deteriorated Regions Based on Anomaly Detection from Rubber Material Electron Microscope Images
-- Verification of Feature Representations Extracted from Deep Learning Models --
Masanao Matsumoto, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ) |
This paper presents an anomaly detection method for estimation of deteriorated regions from rubber material electron mic... [more] |
MMS2021-9 ME2021-9 AIT2021-9
pp.43-46 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
14:50 |
Online |
Online |
Production and Evaluation of Data Set for Semantic Segmentation of 3D CG Image by H.265/HEVC
Norifumi Kawabata (Tokyo Univ. of Science) |
As one of purpose of study on image segmentation, we are able to consider whether between object and background region c... [more] |
|
| IEICE-MI, IEICE-IE, IEICE-SIP, IEICE-BioX, IST, ME [detail] |
2020-05-28
15:40 |
Online |
Online |
People counting device using real-time face detection by AI camera
Koki Takebe, Junichi Akita (Kanazawa Univ) |
Recent progress of semiconductor technology has been enabling small and inexpensive devices to execute the neural networ... [more] |
IST2020-31 ME2020-82
pp.37-40 |
| IEICE-MI, IEICE-IE, IEICE-SIP, IEICE-BioX, IST, ME [detail] |
2020-05-29
14:30 |
Online |
Online |
A method for analyze causes of deterioration of predict quality when Deep Learning is applied to instance segmentation
Tomonori Kubota, Takanori Nakao, Masafumi Katoh, Eiji Yoshida, Hidenobu Miyoshi (Fujitsu Lab.) |
In this paper, we propose a method to analyze the cause of deterioration of prediction accuracy in instance segmentation... [more] |
|
| AIT, IIEEJ, AS, CG-ARTS |
2020-03-13
15:30 |
Tokyo |
Tokyo University of Technology
(Cancelled) |
Toward script translation in calligraphic works using deep learning
-- Character recognition of seal scripts --
Shohei Ninomiya, Masanori Nakayama, Atsushi Miyazawa, Issei Fujishiro (Keio Univ.) |
Calligraphic scripts are classified broadly into five types: seal, clerical, cursive, running, and standard. In this stu... [more] |
AIT2020-69
pp.75-78 |
| AIT, IIEEJ, AS, CG-ARTS |
2020-03-13
14:20 |
Tokyo |
Tokyo University of Technology
(Cancelled) |
Speaker Identification for Evaluating Speaking Activities in Seminar using Deep Learning
Tomoki Akita, Norimasa Yoshida (Nihon Univ.) |
In seminars, students are expected to participate actively. In this study, we would like to create a system that can me... [more] |
AIT2020-144
pp.313-314 |
| HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2020-02-27
14:00 |
Hokkaido |
Hokkaido Univ.
(Cancelled) |
An Image Transformation Network for Privacy-Preserving Deep Neural Networks
Hiroki Ito, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro. Univ.) |
We propose an image transformation network to generate visually-protected images for privacy-preserving deep neural netw... [more] |
|