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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EMCJ, MICT (Joint) |
2020-03-13 11:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Cancelled but technical report was issued) |
Human motion classification by convolutional neural network using signal strength of WBAN Shintaro Sano, Aoyagi Takahiro (TokyoTech)  MICT2019-53 |
In this report, human motion classification by convolutional neural networks (CNNs) using the signal strength of WBANs (W... [more] | MICT2019-53 pp.5-9 |
| AI, IPSJ-ICS, JSAI-SAI, JSAI-DOCMAS, JSAI-KBS (Joint) |
2020-03-08 18:00 |
Hokkaido | Proposition from CNN based feature extraction of multi-channel data through data-colorization technique Komei Hiruta, Eichi Takaya (Keio Univ.), Kazuki Ito, Hiroki Aramaki (NetOne), Takao Inagaki, Norio Yamagishi (TOYOTA), Satoshi Kurihara (Keio Univ.) AI2019-55 |
(To be available after the conference date) [more] | AI2019-55 pp.7-12 |
|
| DC | 2020-02-26 10:25 |
Tokyo | Defective Chip Prediction Modeling Using Convolutional Neural Networks Ryunosuke Oka, Satoshi Ohtake (Oita Univ.), Kouichi Kumaki (Renesas) DC2019-87 |
In recent years, the cost of LSI testing which guarantees reliability has relatively increased due to the development of... [more] | DC2019-87 pp.7-12 |
|
| NC, MBE | 2019-12-06 16:55 |
Aichi | Toyohashi Tech | Evaluation of the visualization techniques providing explanations for decisions of convolutional neural networks Mizuki Mori, Hiroki Tanaka (Kyoto-Sangyo Univ) MBE2019-59 NC2019-50 |
Recent work has proposed a variety of techniques to visualize what a convolutional neural networks (CNN) utilizes to cla... [more] | MBE2019-59 NC2019-50 pp.85-88 |
| NS, ICM, CQ, NV (Joint) |
2019-11-21 10:20 |
Hyogo | Rokkodai 2nd Campus, Kobe Univ. | Visual Analytics for Anomaly Classification in LAN Based on Deep Convolutional Neural Network Yuwei Sun, Hideya Ochiai, Hiroshi Esaki (UTokyo) NS2019-121 |
Recently, the attack monitored in Local Area Network (LAN) is surging. There are some methods being used to analyze the ... [more] | NS2019-121 pp.7-12 |
| EMM | 2019-03-13 15:15 |
Okinawa | TBD | [Poster Presentation]
A Consideration on Spatio-Temporal Feature Learning for Video Forgery Detection Shoken Ohshiro (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2018-104 |
The purpose of our work is to detect objects tampered in the spatial domain of videos including dynamic scenes such as a... [more] | EMM2018-104 pp.67-72 |
| ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] | 2019-02-20 13:30 |
Hokkaido | Hokkaido Univ. | Evaluation of Multi-level Data Demodulation Using Convolutional Neural Networks for Holographic Data Storage Yutaro Katano, Tetsuhiko Muroi, Nobuhiro Kinoshita, Norihiko Ishii (NHK) |
Holographic data storage (HDS) is a promising next generation archival memory with large capacity, high data-transfer ra... [more] | |
| EMM | 2019-01-11 09:55 |
Miyagi | Tohoku Univ. | Study on Digital Audio Watermarking Method Based on Singular Spectrum Analysis with Automatic Parameter Estimation Using a Convolutional Neural Network Kasorn Galajit (JAIST), Jessada Karnjana (NECTEC), Pakinee Aimmanee (SIIT), Masashi Unoki (JAIST) EMM2018-86 |
An audio watermarking method based on the singular-spectrum analysis (SSA) with a convolutional neural network (CNN) for... [more] | EMM2018-86 pp.25-30 |
| SIS | 2018-12-07 09:30 |
Yamaguchi | Hagi Civic Center | A Switching Noise Removal Filter Based on Convolutional Neural Networks and Its Application to Random-valued Impulse Noise Yukiya Fukuda, Ryosuke Kubota (NIT, UC) SIS2018-29 |
In order to remove random-valued impulse noise (RVIN) on a color image, we propose a novel switching denoising filter ba... [more] | SIS2018-29 pp.41-46 |
| IE | 2018-06-29 10:20 |
Okinawa | Single-image Rain Removal Using Residual Deep Learning Takuro Matsui, Masaaki Ikehara, Takanori Fujisawa (Keio Univ.) IE2018-23 |
Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal prob... [more] | IE2018-23 pp.13-18 |
|
| NLP | 2018-04-27 15:35 |
Kumamoto | Kumaoto Univ. | Classification of discrete sequences using transfer learning Masato Ogata, Tsuyoshi Matsuoka (Kyushu Sangyo Univ.) NLP2018-25 |
We introduce a grayscale image in which brightness and pattern of pixels are determined by samples of a discrete sequenc... [more] | NLP2018-25 pp.121-126 |
| PRMU, BioX | 2018-03-18 11:10 |
Tokyo | Simultaneous Learning Model of Food Image Recognition and Ingrediensts Estimation Koyo Ito, Takao Yamanaka (Sophia Univ.) BioX2017-38 PRMU2017-174 |
In recent years, many health-care applications such as food diary have been developed for smart devices. It is important... [more] | BioX2017-38 PRMU2017-174 pp.13-18 |
|
| PRMU, BioX | 2018-03-18 13:55 |
Tokyo | Feature extraction of object shape from motion parallax using convolutional neural network ChengJun Shao, Makoto Murakami (Toyo Univ.) BioX2017-41 PRMU2017-177 |
The convolution neural networks (CNN) have good feature extraction capability. In this paper, we propose a method which ... [more] | BioX2017-41 PRMU2017-177 pp.31-36 |
|
| SANE | 2018-01-25 14:50 |
Nagasaki | Nagasaki Prefectural Art Museum | Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-92 |
The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages:... [more] | SANE2017-92 pp.35-40 |
| MVE | 2017-10-20 11:20 |
Hokkaido | KitamiInstitute of Technology | Estimating the attractiveness of a food photo using a Convolutional Neural Network Akinori Sato (Nagoya Univ.), Keisuke Doman (Chukyo Univ.), Takatsugu Hirayama, Ichiro Ide, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase (Nagoya Univ.) MVE2017-32 |
We have previously proposed a method for estimating the attractiveness of a food photo in order to assist a user to shoo... [more] | MVE2017-32 pp.107-111 |
| PRMU | 2017-10-12 09:30 |
Kumamoto | Accelerating Convolutional Neural Networks Using Low-Rank Tensor Decomposition Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota (Tokyo Inst. of Tech.) PRMU2017-63 |
In the image recognition using convolution neural networks (CNN), convolution operations occupies the majority of the co... [more] | PRMU2017-63 pp.1-6 |
|
| PRMU | 2017-10-13 09:15 |
Kumamoto | Improvement of speed using low precision arithmetic in deep learning and performance evaluation of accelerator Hiroki Naganuma, Akira Sekiya, Kazuki Osawa, Hiroyuki Ootomo, Yuji Kuwamura, Rio Yokota (Tokyo Inst. of Tech.) PRMU2017-81 |
While recent convolution neural networks (CNN)cite{ref:CNN} are improving performance, amout of computation and data vol... [more] | PRMU2017-81 pp.101-107 |
|
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 15:50 |
Tokyo | Face Image Generation System Using Attribute information with DCGANs Yurika Sagawa, Masafumi Hagiwara (Keio Univ.) PRMU2017-52 IBISML2017-24 |
In this paper, we propose an attribute added face image generation system using Deep Convolutional Generative Adversaria... [more] | PRMU2017-52 IBISML2017-24 pp.107-112 |
|
| SANE | 2017-08-24 13:50 |
Osaka | OIT UMEDA Campus | Deep Learning for Target Classification from SAR Imagery
-- Data Augmentation and Translation Invariance -- Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30 |
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (... [more] | SANE2017-30 pp.13-17 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-23 17:15 |
Okinawa | Okinawa Institute of Science and Technology | Visibility Prediction of Color Scheme with the Model of Human Color Vision composed of Convolutional Neural Networks Shodai Sasaki, Yoshihisa Shinozawa (Keio Univ.) NC2017-11 |
In this research, we implement convolutional neural networks (CNN) and introduce a multi-stage color (MSC) model, which ... [more] | NC2017-11 pp.39-44 |
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