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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2022-01-25 16:11 |
Online | Online | Uveitis Lesion Detection Using Ultra-Wide Field Fluorescein Angiography Imaging Registration Tomoki Wakitani (Univ. of Shiga Prefecture), Yuji Hatanaka (Oita Univ.), Hiroshi Keino (Kyorin Univ.), Wataru Sunayama (Univ. of Shiga Prefecture)  MI2021-49 |
Uveitis is a group of diseases that cause inflammation of the uvea and may lead to blindness. Fluorescein fundus angiogr... [more] | MI2021-49 pp.28-31 |
| SRW, SeMI, CNR (Joint) |
2021-11-26 15:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
A Study on Removal of Train Running Noise using U-Net for Spectrogram Images Motoki Ichikawa, Shota Sano, Jian Lin, Yuusuke Kawakita, Tsuyoshi Miyazaki, Hiroshi Tanaka (KAIT) SRW2021-49 SeMI2021-48 CNR2021-23 |
In this manuscript, we present the results of a study on the effect of speech denoising using spectrogram images obtaine... [more] | SRW2021-49 SeMI2021-48 CNR2021-23 pp.74-78(SRW), pp.61-65(SeMI), pp.51-55(CNR) |
| MI, MICT [detail] | 2021-11-05 11:05 |
Online | Online | [Short Paper]
Description of microvessel structures in 3D reconstructed microscopic pathological images of pancreatic cancer Yuka Ishimaki, Tatsuya Yokota, Kugler Mauricio (NITech), Kenoki Ohuchida (KU), Hidekata Hontani (NITech) MICT2021-33 MI2021-31 |
In this manuscript, we propose a method that segments microvascular regions in a 3D pathological image. For this purpose... [more] | MICT2021-33 MI2021-31 pp.26-27 |
| MI | 2021-07-08 14:00 |
Online | Online | Unsupervised deep learning with low-rank and sparse priors for blood vessel enhancement from free-breathing angiography Ryoji Ishibashi, Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2021-11 |
(To be available after the conference date) [more] | MI2021-11 pp.11-14 |
| MI | 2021-03-15 15:30 |
Online | Online | Evaluation of Bayesian Active Learning for Segmentation of Liver and Spleen in Large Scale Abdominal MR Data Sets Bin Zhang, Yoshito Otake, Mazen Soufi (NAIST), Masatoshi Hori (Kobe University), Noriyuki Tomiyama (Osaka University), Yoshinobu Sato (NAIST) MI2020-60 |
Manual annotation in image segmentation is time-consuming and expensive. In order to obtain large number of annotated da... [more] | MI2020-60 pp.62-65 |
| PRMU, IPSJ-CVIM | 2021-03-05 14:10 |
Online | Online | A Consideration on Suspicious Object Detection by Mixup and Improved U-Net Naruki Kanno, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato (Waseda Univ.) PRMU2020-90 |
In this paper, on suspicious object detection by using semantic segmentation, we study the effectiveness of Mixup data a... [more] | PRMU2020-90 pp.121-126 |
| NC, MBE (Joint) |
2021-03-04 09:25 |
Online | Online | An Estimated Intersections Reduction Method for Percussion Source Separation Based on the U-Net Daisuke Tanaka, Susumu Kuroyanagi (NIT) NC2020-55 |
In the music information processing using drum information, the sound source separation is pre-processed to separate onl... [more] | NC2020-55 pp.71-76 |
| MVE, IMQ, IE, CQ (Joint) [detail] |
2021-03-01 13:50 |
Online | Online | Image Compression based on Deep Learning with Visual Spatial Frequency Characteristics Naoki Tada (Chiba Univ.), Tatsuki Adaniya (ADAWARP JAPAN Inc.), Keita Hirai (Chiba Univ.) IMQ2020-13 IE2020-53 MVE2020-45 |
(To be available after the conference date) [more] | IMQ2020-13 IE2020-53 MVE2020-45 pp.17-18 |
| SC | 2020-05-29 15:30 |
Online | Online | [Poster Presentation]
Tumor detection from colonoscopy Whole Slice Images By Deep Learning Cherubin Mugisha, Incheon Paik (School of Computer Science and Engineering) |
Image semantic segmentation is a technique of segregating an image into many parts. The goal of this research was to use... [more] | |
| MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2020-05-28 13:40 |
Online | Online | SIP2020-2 BioX2020-2 IE2020-2 MI2020-2 |
(To be available after the conference date) [more] | SIP2020-2 BioX2020-2 IE2020-2 MI2020-2 pp.5-8 |
| CW (2nd) |
2020-03-03 | Tokyo | Real-time manipulation of character face images using GAN Shumpei Maruyama, Naiwala P. Chandrasiri (Kogakuin Univ) |
In this research, we have created a system that can be used as a character manipulation applications using machine learn... [more] | ||
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-27 14:15 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
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) |
This paper presents an automated distress region detection method using subway tunnel images. We previously proposed a m... [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 |
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