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Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 11件中 1~11件目  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-27
14:15
Hokkaido Hokkaido Univ. A note on detection of distress regions in subway tunnels by using U-net based network
An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ)
 [more]
IMQ 2019-12-20
16:20
Tokyo   A Preliminary Study on BTF Image Database Generation using Deep Learning
Naoki Tada, Keita Hirai (Chiba Univ.) IMQ2019-10
A method using Bidirectional Texture Function (BTF) is one of the methods to reproduce a realistic image in Computer Gra... [more] IMQ2019-10
pp.3-8
EA, EMM 2019-11-22
15:30
Ishikawa Kanazawa Institute of Technology EA2019-60 EMM2019-88 In this paper, we propose a time-domain audio source separation method using down-sampling and up-sampling layers based ... [more] EA2019-60 EMM2019-88
pp.41-48
PRMU 2019-10-18
14:50
Tokyo   From Cats to Cars: A Data Set Generation Strategy for U-net based Autonomous Driving
Hiroki Hiraoka, Atsushi Imiya (Chiba Univ.) PRMU2019-38
The deep convolutional neural networks (DCNNs)
require data to supervise the network structures.
A semi-automatic da... [more]
PRMU2019-38
pp.35-40
MVE 2019-10-10
14:50
Hokkaido   Segnet and U-Net Implementations for Water Hyacinth Semantic Segmentation in Thailand
Supatta Viriyavisuthisakul, Parinya Sanguansat (PIM), Toshihiko Yamasaki (UTokyo) MVE2019-25
Water Hyacinth is an aquatic weed that can spread very quickly. Normally, it can be found in a dam or river. Water Hyaci... [more] MVE2019-25
pp.9-12
MI 2019-07-05
14:00
Hokkaido Future Univ. Hakodate [Short Paper] Automated extraction of cartilage regions on knee MR images by deep learning approach
Koki Fukaya, Takeshi Hara, Xiangrong Zhou (Gifu Univ.), Taiki Nozaki, Masaki Matsusako (St. Luke's HP), Hiroshu Fujita (Gifu Univ.) MI2019-17
(To be available after the conference date) [more] MI2019-17
pp.1-2
NC, MBE
(Joint)
2019-03-04
09:55
Tokyo University of Electro Communications The percussion instruments separation method using the Deep U-Net
Yosuke Ito, Kuroyanagi Susumu (NIT) NC2018-44
Modern popular music is often mixed with harmonic musical instruments and percussion instruments. Therefore, when analyz... [more] NC2018-44
pp.1-6
MI 2019-01-22
10:05
Okinawa   Super-resolution of μCT image about dissected lung tissue using Adversarial Dense U-net
Tong Zheng, Hirohisa Oda, Holger R. Roth, Masahiro Oda, Shota Nakamura (Nagoya University), Kensaku Mori (Nagoya University/NII) MI2018-61
μCT images capture three dimensional structures of tissues with a very high resolution of 100 micrometer or smaller. fin... [more] MI2018-61
pp.7-12
MI 2019-01-23
14:00
Okinawa   Segmentation for diffuse lung disease opacities on CT images using U-Net and residual U-Net
Kanako Murakami, Shoji Kido, Yasushi Hirano, Shingo Mabu (Yamaguchi Univ.), Kenji Kondo (AIST/Panasonic), Jun Ozawa (AIST) MI2018-102
Segmentation is important for diagnosis of diffuse lung diseases (DLD) as same as classification. In recent years, a lot... [more] MI2018-102
pp.175-179
PRMU, IBISML, IPSJ-CVIM [detail] 2018-09-21
09:40
Fukuoka   [Short Paper] Automatic Segmentation of Epicardial Using Deep Learning
Ziyu Zhao, Tomoe Otoishi, Yutaro Iwamoto (Ritsumei Univ), Youji Tetsuka, Yuki Okada, Kiyosumi Maeda, Atsuyuki Wada, Atsunori Kashiwagi (Kusatsu General Hospita), Yanwei Chen (Ritsumei Univ) PRMU2018-55 IBISML2018-32
The epicardial is a wall sac containing the heart and the roots of the great vessels. Epicardial adipose tissue adhere t... [more] PRMU2018-55 IBISML2018-32
pp.131-132
MBE, NC
(Joint)
2018-03-13
11:15
Tokyo Kikai-Shinko-Kaikan Bldg. Application of U-Net to spine image extraction in CT image
Mikoto Kamata, Masayuki Kikuchi (Tokyo Univ.of Tech.), Hayaru Shouno (Univ. of Electro-Communications.), Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (Fuzzy Logic Systems Inst.) NC2017-81
In this study, we aimed at automatic extraction of spinal parts in CT images using deep learning as a foothold for autom... [more] NC2017-81
pp.81-84
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