| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI | 2024-01-18 13:30 |
Yamanashi | Raki House Kaiji | Enhancing Human Skeleton Estimation with Multi-Frame mmWave Radar Point Cloud-based Method Xintong Shi, Tomoaki Ohtsuki (Keio Univ.)  SeMI2023-52 |
Millimeter-Wave (mmWave) radar-based skeleton estimation has emerged as a focal point in the realm of human motion analy... [more] | SeMI2023-52 pp.18-21 |
| WIT | 2023-06-17 15:20 |
Okinawa | Okinawa Industry Support Center (Primary: On-site, Secondary: Online) |
Improved Visual Intentions Estimation Model Using Depth Information and Object Detection Sho Higa, Masakiyo Okuhama, Koji Yamada (Ryukyu Univ.), Shihoko Kamisato (NIT,Okinawa College) WIT2023-17 |
In this study, we propose an improved visual intention estimation model based on object detection to distinguish eye and... [more] | WIT2023-17 pp.76-77 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-17 14:05 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
A Consideration on Improving Prediction Accuracy of PredNet by Combining Features of Pseudo-future Frame. Kanato Sakama, Shunichi Sekiguchi, Wataru Kameyama (Waseda Univ.) IMQ2022-81 IE2022-158 MVE2022-111 |
We have been studying the applicability of PredNet, one of the DNN-based video frame prediction methods proposed to prov... [more] | IMQ2022-81 IE2022-158 MVE2022-111 pp.315-320 |
| CNR, BioX | 2023-03-02 11:30 |
Oita | (Primary: On-site, Secondary: Online) |
A Study on Single Modal Ear Acoustic Personal Authentication System Sora Takagi (Kansai Univ.), Shunsuke Kita (ORIST), Yoshinobu Kajikawa (Kansai Univ.) BioX2022-73 CNR2022-39 |
Biometric authentication requires the provision of the user's personal biometric information.Currently, the mainstream b... [more] | BioX2022-73 CNR2022-39 pp.65-68 |
| MSS, NLP | 2022-03-28 13:25 |
Online | Online | A Study on Convolutional LSTM Based Weather Forecasting Method Using Colored Cloud Images Haruki Takehana, Astuo Ozaki (OIT) MSS2021-62 NLP2021-133 |
In recent years, there has been an increase in flood damage caused by localized heavy rainfall, and research on forecast... [more] | MSS2021-62 NLP2021-133 pp.37-42 |
| NC, NLP (Joint) |
2021-01-21 12:05 |
Online | Online | Examination of precipitation estimation using atmospheric variables Takanori Ito, Motoki Amagasaki, Kei Ishida, Masato Kiyama, Masahiro Iida (GSST Kumamoto University) NC2020-34 |
In this paper, we developed a model for SR using ConvLSTM to improve the resolution of precipitation data. In the relat... [more] |
NC2020-34 pp.13-17 |
| MVE | 2019-08-29 15:50 |
Aichi | A Proposal of Facial Expression Recognition Method from Video Using Enhanced ConvLSTM Ryo Miyoshi (Chukyo Univ.), Noriko Nagata (Kwansei Gakuin Univ.), Manabu Hashimoto (Chukyo Univ.) MVE2019-12 |
We propose an enhanced convolutional long short-term memory (ConvLSTM) algorithm, i.e., Enhanced ConvLSTM, by adding ski... [more] | MVE2019-12 pp.37-42 |
|
| AI, IPSJ-ICS, JSAI-KBS, JSAI-DOCMAS, JSAI-SAI | 2019-03-09 17:00 |
Hokkaido | Please fill in Kouki Yoneda, Souichiro Yokoyama, Tomohisa Yamashita, Hidenori Kawamura (Hokudai) AI2018-57 |
(To be available after the conference date) [more] | AI2018-57 pp.25-30 |
|
| EMM | 2018-03-05 14:35 |
Kagoshima | Naze Community Center (Amami-Shi, Kagoshima) | [Poster Presentation]
Video Forgery Detection Considering Spatio-Temporal Consistency Takaaki Yoshida (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2017-81 |
This paper proposes a video forgery detection method in dynamic scenes such as dynamic background or dynamic perspective... [more] | EMM2017-81 pp.23-28 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Combination of LSTM and CNN on recognizing mathematical symbols Hai Nguyen Dai, Anh Le Duc, Masaki Nakagawa (TUAT) IBISML2014-73 |
Combining classifiers is an approach that has been shown to be useful on numerous occasions when striving for further im... [more] | IBISML2014-73 pp.287-292 |
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