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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 15:20 |
Online | Online | A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials Rintaro Yanagi, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. A... [more] | |
| MI, MICT [detail] | 2021-11-05 15:50 |
Online | Online | [Short Paper]
Sketch-based CT image generation of lung cancers using Pix2pix
-- An attempt to improve representation by adopting Style Blocks -- Ryo Toda, Atsushi Teramoto (FHU), Masakazu Tsujimoto (FHUH), Hiroshi Toyama, Masashi Kondo, Kazuyoshi Imaizumi, Kuniaki Saito (FHU), Hiroshi Fujita (Gifu Univ.)  MICT2021-42 MI2021-40 |
Generative adversarial networks (GAN) have been used to overcome the lack of data in medical images. However, such appli... [more] | MICT2021-42 MI2021-40 pp.66-67 |
| EMM | 2021-03-04 13:30 |
Online | Online | [Poster Presentation]
Object Boundary Correction for Monocular Depth Estimation Using Region Segmentation Ikuma Yasukawa, Shoko Imaizumi (Chiba Univ.) EMM2020-67 |
We propose a new method to generate high-quality depth images in this paper. The proposed method corrects object boundar... [more] | EMM2020-67 pp.1-6 |
| AI | 2021-02-22 13:20 |
Online | Online | AI2020-44 |
(To be available after the conference date) [more] | AI2020-44 pp.30-35 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2021-02-19 14:15 |
Online | Online | [Special Talk]
A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. I... [more] | |
| SANE | 2020-11-25 10:45 |
Online | Online | Generation of learning images corresponding to differences in ground penetrating radar and underground media for highly accurate identification of ground penetrating radar images with AI Daiki Taga, Tomoyuki Kimoto (NIT, Oita), Jun Sonoda (NIT, Sendai) SANE2020-28 |
Ground penetrating radar is a technology that detects underground objects by utilizing the reflection of radio waves inc... [more] | SANE2020-28 pp.7-12 |
| PRMU, IPSJ-CVIM | 2020-03-16 11:00 |
Kyoto | (Cancelled but technical report was issued) |
Multimodal Recipe Search and Image Generation by Disentangling Contents and Styles Yu Sugiyama, Keiji Yanai (UEC) PRMU2019-70 |
(To be available after the conference date) [more] | PRMU2019-70 pp.27-32 |
| NC, MBE (Joint) |
2020-03-05 10:45 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
YuruGAN: Yuru-Charas Generated by Generative Adversarial Networks Yuki Hagiwara, Toshihisa Tanaka (TUAT) NC2019-93 |
Yuru-chara is a mascot character created by local governments and companies for the purpose of publicizing information o... [more] | NC2019-93 pp.101-106 |
| HCGSYMPO (2nd) |
2019-12-11 - 2019-12-13 |
Hiroshima | Hiroshima-ken Joho Plaza (Hiroshima) | Investigating transformation of 3D faces in terms of their masculinity/femininity impressions Yuki Ozawa, Nami Takeyama, Isseki Miwa, Shigeru Akamatsu (Hosei Univ.) |
We investigated the nature of impression manipulation of gender differences on 3D faces. First, we investigated the diff... [more] | |
| IE, CS, IPSJ-AVM, ITE-BCT [detail] | 2019-12-06 10:10 |
Iwate | Aiina Center | Adversarial Examples for Monocular Depth Estimation Koichiro Yamanaka, Ryutaroh Matsumoto, Keita Takahashi, Toshiaki Fujii (Nagoya Univ.) CS2019-83 IE2019-63 |
Adversarial examples for classification and object recognition problems using convolutional neural net- works (CNN) have... [more] | CS2019-83 IE2019-63 pp.91-95 |
| AI | 2019-11-28 13:30 |
Fukuoka | On a Method for Constructing a Mahjong Tile Detector from Abstract Training Data Ryosuke Suzuki, Tadachika Ozono, Toramatsu Shintani (NIT) AI2019-31 |
Constructing a detector needs constructing dataset which consists various scenes. We generate synthesize images based on... [more] | AI2019-31 pp.7-12 |
|
| HCS | 2019-10-26 15:25 |
Tokyo | Nihon Univ. | Food Image Segmentation Dataset and Its Applications Kaimu Okamoto, Cho Jaehyeong, Takumi Ege, Keiji Yanai (UEC) HCS2019-50 |
(To be available after the conference date) [more] | HCS2019-50 pp.59-64 |
| 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 |
|
| PRMU, BioX | 2019-03-17 15:15 |
Tokyo | Arbitrary Charactor Image Generation in Arbitrary Poses using Neural Network Hidemoto Nakada, Hideki Asoh (AIST) BioX2018-41 PRMU2018-145 |
Thanks to recent improvement of image generation technologies by neural networks, now we can gener- ate photo-realistic ... [more] | BioX2018-41 PRMU2018-145 pp.73-78 |
|
| PRMU, BioX | 2019-03-18 10:00 |
Tokyo | A Generative Self-Ensemble Approach to Simulated+Unsupervised Learning Yu Mitsuzumi (Kyoto Univ.), Go Irie (NTT), Atsushi Nakazawa (Kyoto Univ.), Akisato Kimura (NTT) BioX2018-52 PRMU2018-156 |
The simulated and unsupervised (S+U) learning framework is an effective approach in computer vision since it solves vari... [more] | BioX2018-52 PRMU2018-156 pp.137-142 |
|
| PRMU | 2018-10-19 15:40 |
Kanagawa | PRMU2018-70 |
CyberAgent AI Lab is developing technology to assist and automate creative workflows for advertising expressions such as... [more] | PRMU2018-70 p.11 |
|
| CQ, MVE, IE, IMQ (Joint) [detail] |
2018-03-09 11:15 |
Okinawa | Okinawa Industry Support Center | Generate images from food photos by Conditional GAN Yoshifumi Ito, Ryosuke Tanno, Keiji Yanai (UEC) IMQ2017-50 IE2017-142 MVE2017-92 |
Making images using Generative Adversarial Network (GANs) has been actively conducted recently. In this paper, we propo... [more] |
IMQ2017-50 IE2017-142 MVE2017-92 pp.137-142 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 15:50 |
Tokyo | Face Image Generation System Using Attribute information with DCGANs Yurika Sagawa, Masafumi Hagiwara (Keio Univ.) PRMU2017-52 IBISML2017-24 |
In this paper, we propose an attribute added face image generation system using Deep Convolutional Generative Adversaria... [more] | PRMU2017-52 IBISML2017-24 pp.107-112 |
|
| SP, IPSJ-SLP (Joint) |
2017-07-27 14:30 |
Miyagi | Akiu Resort Hotel Crescent | [Invited Talk]
Synthesis, Recognition and Conversion of Various Speech Using Deep Learning and Their Applications Takashi Nose (Tohoku Univ.) SP2017-16 |
This paper focuses on synthesis, recognition and conversion of various speech in the speech processing using deep learni... [more] | SP2017-16 pp.3-8 |
| PRMU, SP | 2017-06-22 14:00 |
Miyagi | PRMU2017-27 SP2017-3 |
(To be available after the conference date) [more] | PRMU2017-27 SP2017-3 pp.13-16 |
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