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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 44  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] 2022-05-20
Kumamoto Kumamoto University
(Primary: On-site, Secondary: Online)
Application of LambdaNetwork learning long-range interactions between pixels to metal artifact detection
Daisuke Shigemori, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.)
Although deep learning-based image transformation has been attempted to be applied to metal artifact reduction, feature ... [more]
HI, IEICE-HIP, ASJ-H, VRPSY [detail] 2022-02-27
Online on line An analysis of microsaccades and attentional direction induced by bottom-up attention shifts
Masahito Sakaguchi, Ryoma Kobata, Hisashi Yoshida, Takeshi Kohama (Kindai Univ.)
This study experimented with inducing bottom-up attention shift to peripheral spotlight stimuli presented at random timi... [more] HI2022-1
HI, IEICE-HIP, ASJ-H, VRPSY [detail] 2022-02-27
Online on line A method for estimating brain regions contributing to sustained attention from fNIRS signals
Junpei Kubo, Kazuki Tsuji, Hisashi Yoshida, Takeshi Kohama (Kindai Univ.)
This study aims to develop a neurorehabilitation method for attention function. We proposed estimating the responsible a... [more] HI2022-5
AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] 2022-02-21
Online online Statistical Analysis on Estimation of Laparoscopic Image Region of Interest Including Contrast Enhancement Based on Saliency Map
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.)
In the medical field such as hospital, there are many chance for medical workers to see medical images visually. Therefo... [more]
AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] 2022-02-22
Online online A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents a note on accurate distress classification using deep learning considering confidence in attention m... [more] MMS2022-33 ME2022-58 AIT2022-33
AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] 2022-02-22
Online online A Note on Distress Detection based on Deep Learning with Hierarchical Multi-Scale Attention Mechanism for Supporting Maintenance of Subway Tunnels
Saya Takada, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
In maintenance of transportation infrastructures, advanced support technologies that can reduce the burden on engineers ... [more] MMS2022-34 ME2022-59 AIT2022-34
ME 2021-12-13
Online online Emotion Recognition from Gait Using Attention Spatial-Temporal Graph Convolutional Network
Shoji Kisita, Chen Yen-Wei (Ritsumeikan Univ.), Tomoko Tateyama (Shiga Univ.), Yutaro Iwamoto, Liu Jiaqing, Chai Shurong (Ritsumeikan Univ.)
3D pose recognition is a technology that has been used in many fields, including touchless operation in the medical fiel... [more] ME2021-94
BCT, IEEE-BT 2021-09-03
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
HI 2021-03-05
Online   Estimation of brain regions contributing to maintain focal attention based on fNIRS measurements
Tomoki Matsumoto, Jyunpei Kubo, Takeshi Kohama, Hisashi Yoshida (Kindai Univ.)
Neurorehabilitation researches, which aims to recover from brain dysfunction, has attracted attention as a method to eff... [more] HI2021-3
HI 2021-03-05
Online   An analysis of microsaccade characteristics induced by bottom-up attention shifts
Shogo Noguchi, Masahito Sakaguchi, Ryoma Kobata, Takeshi Kohama, Hisashi Yoshida (Kindai Univ.)
The purpose of this study is to investigate the relationship between bottom-up attention shifts and characteristics of m... [more] HI2021-4
HI 2021-03-05
Online   [Short Paper] Fault determination of electric motor coil by YOLO-v3 with Attention
Mizuki Kato (Ritsumeikan Univ Grad), Yutaro Iwamoto (Ritsumeikan Univ), Toshitaka Sugimoto, Toru Aiba (Kusatsu Electric), Yen-Wei Chen (Ritsumeikan Univ)
Object detection has been widely applied to the visual inspection of factory products. However, since the detection mode... [more] HI2021-7
IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] 2021-02-18
Online Online A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level... [more] MMS2021-4 ME2021-4 AIT2021-4
HI, VRPSY 2020-11-27
Online   Different effects of multisensory integration on three attention networks
Guo Xuanru (Kyushu Univ), Tang Xiaoyu (LNNU), Takeharu Seno (Kyushu Univ)
Multisensory integration affects attention in many ways. However, it is unclear whether multisensory integration has the... [more] HI2020-60
IEICE-MI, IEICE-IE, IEICE-SIP, IEICE-BioX, IST, ME [detail] 2020-05-29
Online Online Toward Estimation of Attentional Direction Using EEG Signals during Simultaneous Presentation of Two Types of Music
Kana Mizokuchi, Toshihisa Tanaka (TUAT), Takashi G. Sato, Yoshifumi Shiraki (NTT)
People can pay selective attention to music or speech from various sounds.It has been reported that when multiple beat s... [more]
HI, 3DMT 2020-03-11
Tokyo Kogakuin Univ. Tokyo Urban Tech Tower Campus
Higher-order textures that attract attention -- Pop-out of material surfaces --
Kento Yoneoka (Tsukuba Univ.), Nobuhiko Wagatsuma (Toho Univ.), Ko Sakai (Tsukuba Univ.)
Pop-out among textures depends on low-lever image features and has been known as an outcome of the neural mechanisms und... [more] HI2020-50 3DIT2020-1
HI, VRPSY 2019-11-08
Osaka Kindai Univ. An effect of bottom-up attention on microsaccade rate
Shogo Noguchi, Takeshi Kohama, Hisashi Yoshida (Kindai Univ.)
Focusing or reallocating attention while actively fixating on the visual stimuli modulates the rate of microsaccades. Ho... [more] HI2019-78
ME, IEICE-IE, IEICE-ITS, MMS, HI, AIT [detail] 2019-02-20
Hokkaido Hokkaido Univ. A note on estimation of inspectors' visual attention using distress images of subway tunnels -- Trial introduction of deep learning-based saliency prediction methods --
Ryota Saito, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents the first trial for estimating inspectors' visual attention of distress images in subway tunnels. Th... [more] MMS2019-30 HI2019-30 ME2019-52 AIT2019-30
ME 2019-02-09
Kanagawa Kanto Gakuin University Investigation of realization of space for improvement of concentration ability using MR technology
Kanta Michioka, Akira Kubota (Chuo Univ.)
We aimed to realize an MR space that improves concentration on the desk by limiting the effective visual field of the us... [more] ME2019-10
IIEEJ, AIT 2018-08-10
Nagano Shinshu Univ. Gaze Estimation based on Visual Attention Model
Sho Oi (Ritsumeikan Univ.), Mutsuo Sano (OIT), Hajime Tabuchi, Fumie Saito, Satoshi Umeda (Keio Univ.)
Gaze information about human is important to judge the attention function in the human. For example, gaze information du... [more] AIT2018-167
IDY, IEICE-EID, SID-JC [detail] 2018-07-30
Tokyo Kikai-Shinko-Kaikan Bldg. [Invited Lecture] Comparison between Binocular and Monocular Augmented Reality Presentation when Continuous Viewing is Required
Akihiko Kitamura, Yasunori Kinosada (Osaka Univ.), Takahiko Kimura (Kansai Univ. of Welfare Sci.), Kazumitsu Shinohara (Osaka Univ.), Takashi Sasaki, Haruhiko Okumura, Aira Hotta (Toshiba corp.)
We compared binocular augmented reality (AR) presentation and monocular AR presentation in a situation where continuous ... [more] IDY2018-38
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