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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 53  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] 2022-05-20
16:20
Kumamoto Kumamoto University Kurokami Campus
(Primary: On-site, Secondary: Online)
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis
Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-28 BioX2022-28 IE2022-28 MI2022-28
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] SIP2022-28 BioX2022-28 IE2022-28 MI2022-28
pp.144-149
MBE, NC
(Joint)
2022-03-02
09:30
Online Online A Study on Improvement of Recognition Accuracy and Speed-up of SOM-based Classification System
Shun Tasaka, Hiroomi Hikawa (Kansai Univ.) NC2021-46
This paper discusses a new type of image classifier called class-SOM, which is based on self-organizing map (SOM).
The... [more]
NC2021-46
pp.1-4
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-22
11:10
Online Online Enhancing Personalized Food Image Classifier by Visual Attention and Class-Dependent Weighting
Seum Kim, Yoko Yamakata, Kiyoharu Aizawa (UTokyo) ITS2021-47 IE2021-56
In a real-world setting, food records are very noisy and strongly imbalanced. Besides, inter-class similarity and intra-... [more] ITS2021-47 IE2021-56
pp.133-138
MI 2022-01-26
10:13
Online Online [Short Paper] Abnormality Detection for Covid-19 Chest CT Images by Dimensionality Reduction Based on Contrastive Learning
Hiroki Tobise, Kugler Mauricio, Tatsuya Yokota (NITech), Masahiro Hashimoto (Keio Univ.), Yoshito Otake (NAIST), Toshiaki Akashi (Juntendo Univ.), Akinobu Shimizu (TUAT), Hidekata Hontani (NITech) MI2021-53
In this article, we propose a method that detects anomaly regions in chest CT images for the aid of Covid-19 diagnosis. ... [more] MI2021-53
pp.41-42
PRMU 2021-12-17
14:45
Online Online Data Selection for Efficient Deep Learning
Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2021-51
We are investigating the method to sample the important data from the whole dataset for efficient training of Deep Neura... [more] PRMU2021-51
pp.148-153
MI 2021-07-09
11:00
Online Online [Short Paper] Construction of Subtype Classifier for Malignant Lymphoma based on H&E-stained Images using Immuno-stainning Data
Yuki Hirono (NIT), Noriaki Hashimoto (RIKEN), Kugler Mauricio, Tatsuya Yokota (NIT), Miharu Nagaishi (Kurume Univ.), Hiroaki Miyoshi, Koichi Oshima (Kurume Univ./JSP), Ichiro Takeuchi (NIT/RIKEN), Hidekata Hontani (NIT) MI2021-16
In pathological diagnosis of malignant lymphoma, a HE image is observed at first and then a set of immunostained images ... [more] MI2021-16
pp.31-32
SP, IPSJ-SLP, IPSJ-MUS 2021-06-19
15:00
Online Online Development of ultrasonic signal classification system using deep learning
Kosei Ozeki, Naofumi Aoki, Yoshinori Dobashi (Hokkaido Univ.), Kenichi Ikeda, Hiroshi Yasuda (SST) SP2021-21
The problem with sound wave communication is that it causes more incorrect identification than radio wave communication.... [more] SP2021-21
pp.97-100
EMM, IT 2021-05-21
13:10
Online Online A Study of Detecting Adversarial Examples Using Sensitivities to Multiple Auto Encoders
Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) IT2021-11 EMM2021-11
By removing the small perturbations involved in adversarial examples, the image classification result returns to the cor... [more] IT2021-11 EMM2021-11
pp.60-65
MI 2021-03-15
15:15
Online Online Deep State-Space Modeling of FMRI Images with Disentangle Attributes
Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] MI2020-59
pp.56-61
EMM 2021-03-04
14:15
Online Online [Poster Presentation] Detection of Adversarial Examples in CNN Image Classifiers Using Features Extracted with Multiple Strengths of Filter
Akinori Higashi, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) EMM2020-70
Deep learning has been used as a new method for machine learning, and its performance has been significantly improved. A... [more] EMM2020-70
pp.19-24
MVE, IMQ, IE, CQ
(Joint) [detail]
2021-03-01
16:05
Online Online Taste Prediction System from Cooking Images Using Deep Learning
Akinobu Yoshioka, Qiu Chen (kogakuin Univ.) IMQ2020-17 IE2020-57 MVE2020-49
In recent years, a large amount of food images have been uploaded on social media, etc., which have been closed related ... [more] IMQ2020-17 IE2020-57 MVE2020-49
pp.34-39
MBE, MICT 2021-01-28
14:15
Online Online Electroencephalogram classification to motor imagery, execution, and observation of right index finger flexion using convolutional neural network
Yugo Kuramura, Junya Takemoto, Tomohiko Igasaki (Kumamoto Univ.) MICT2020-24 MBE2020-29
We attempted to classify the EEG when the participants performed five tasks related to the right index finger flexion: k... [more] MICT2020-24 MBE2020-29
pp.17-21
PRMU 2020-12-18
15:10
Online Online A Hybrid Sampling Strategy for Improving the Accuracy of Image Classification with less Data
Ruiyun Zhu, Fumihiko Ino (Osaka Univ.) PRMU2020-62
This paper proposes a hybrid sampling strategy to improve learning accuracy with less training data for image classifica... [more] PRMU2020-62
pp.139-144
ITS, WBS, RCC 2020-12-14
13:00
Online Online A Proposal of Information Embedding Method Using Image Classifier for Parallel Transmission Visible Light Communications
Keita Kinpara, Tadahiro Wada, Kaiji Mukumoto (Shizuoka Univ), Hiraku Okada (Nagoya Univ) WBS2020-14 ITS2020-10 RCC2020-17
For visible light communications using a liquid crystal display and an image sensor, it must be desirable to embed trans... [more] WBS2020-14 ITS2020-10 RCC2020-17
pp.35-40
SIS, ITE-BCT 2020-10-01
14:00
Online Online An FPGA Implementation of Human Recognition using MRCoHOG Features
Yuya Nagamine, Kazuki Yoshihiro (Kyutech), Masatoshi Shibata, Hideo Yamada (EQUOS RESEARCH), Shuichi Enokida, Hakaru Tamukoh (Kyutech) SIS2020-16
In this research, we design a hardware of human recognition using Multiresolution Co-occurrence Histograms of Oriented G... [more] SIS2020-16
pp.35-39
EMM 2020-03-05
16:45
Okinawa
(Cancelled but technical report was issued)
[Poster Presentation] Detecting Adversarial Examples Based on Sensitivities to Lossy Compression Algorithms
Akinori Higashi, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) EMM2019-123
The adversarial examples are created by adding small perturbations to an input image for misleading an CNN-based image c... [more] EMM2019-123
pp.113-116
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-27
16:20
Hokkaido Hokkaido Univ.
(Cancelled but technical report was issued)
A Note on Generation of Electron Microscope Images via Auxiliary Classifier Generative Adversarial Network with Mix Proportions
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
In this paper, we investigate a method for generation of images that represent the internal structure of rubber material... [more]
AI 2019-07-22
16:50
Hokkaido   Investigation of Generating Dataset for Recognizing Figures on Augmented Reality
Ryosuke Suzuki, Tadachika Ozono, Toramatsu Shintani (Nitech) AI2019-16
An intelligent augmented reality (AR) system requires comprehending symbols and their contexts in the real world. In thi... [more] AI2019-16
pp.83-88
ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] 2019-02-20
11:00
Hokkaido Hokkaido Univ. A Note on Estimation of Rock Drilling Energy Using Tunnel Working Face Images
Kentaro Yamamoto, Ryosuke Harakawa, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
In tunnel construction, it is important to grasp the geological condition of the rock to shorten the construction period... [more]
MoNA 2018-12-25
14:40
Tokyo   Detection of Predator Animals Features using Machine Learning
Fahad Alharbi, Eiji Kamioka (SIT) MoNA2018-49
In this paper, predator animals feature detection is discussed. Animals recognition is one of the areas in which a limit... [more] MoNA2018-49
pp.61-66
 Results 1 - 20 of 53  /  [Next]  
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