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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2023-05-18 15:30 |
Aichi | Nagoya Congress Center | Grad-CAM approach for Multiclass Magnetic Resonance Imaging Tumor detection and Classification Tahir Hussain, Shouno Hayaru (UEC)  MI2023-4 |
The growth of abnormal cells in the human brain causes brain tumors (BT). Early diagnosis becomes essential for timely t... [more] | MI2023-4 pp.10-13 |
| IN, NS (Joint) |
2023-03-02 13:50 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
A Method for Automatic Generation of 3D Background Models using Fractal Models and Genetic Algorithms Kaito Watanabe, Naoto Hoshikawa (NIT, Oyama College), Hirotaka Nakayama (NAOJ), Tomoyoshi Ito, Atushi Shiraki (Chiba Univ) NS2022-190 |
In recent years, the 3DCG-related industry has grown rapidly and the demand for 3DCG content has increased. However, 3DC... [more] | NS2022-190 pp.133-138 |
| KBSE, SC | 2022-11-04 16:30 |
Nagano | (Primary: On-site, Secondary: Online) |
Fitness Evaluation Based on 3D Model Generation System by Fractal Model and Genetic Algorithm Kaito Watanabe, Naoto Hoshikawa (NIT, Oyama College), Hirotaka Nakayama (NAOJ), Tomoyoshi Ito, Atushi Shiraki (Chiba Univ) KBSE2022-36 SC2022-31 |
In recent years, the 3DCG-related industry has grown rapidly, and the demand for 3DCG content creation has increased, bu... [more] | KBSE2022-36 SC2022-31 pp.31-36 |
| NS | 2021-10-07 09:15 |
Online | Online | Research on procedural technology of 3D landscape image using fractal models and genetic algorithm Kaito Watanabe, Naoto Hoshikawa (Oyama College), Hirotaka Nakayama (NAOJ), Atushi Shiraki, Tomoyoshi Ito (Chiba Univ) |
We propose an automatic generation method of 3D landscape data to meet the growing demand for 3D content and the labor s... [more] | |
| SIS, IPSJ-AVM | 2021-06-24 10:10 |
Online | Online | Improvement of Verification Accuracy Using CNN for Color Halftone Images Yuto Matsuoka, Shoko Imaizumi, Takahiko Horiuchi (Chiba Univ.) SIS2021-3 |
In this paper, we propose an automatic collation method, which improves the verification accuracy against visual compari... [more] | SIS2021-3 pp.13-18 |
| NC, MBE (Joint) |
2021-03-03 13:50 |
Online | Online | Analysis of deep convolutional neural network texture representation using Portilla-Simoncelli statistics Yusuke Hamano, Hayaru Shouno (UEC) NC2020-48 |
Recently, DCNN has achieved significant success in the field of computer vision. It is suggested that the DCNN, which ar... [more] | NC2020-48 pp.31-36 |
| MVE, IPSJ-CVIM | 2021-01-22 15:35 |
Online | Online | [Short Paper]
Basic examination about wiring status judgment of switchboard by image recognition Keishi Nishimoto, Takeshi Hirama (itic.pref.ibaraki.jp) MVE2020-40 |
In the manufacturing industry, especially in the field of wiring work, the shortage of workers is an issue, and even beg... [more] | MVE2020-40 pp.45-46 |
| MICT, MI | 2020-11-04 16:10 |
Online | Online | Performance of automated melanoma diagnosis by adding lesion information to deep convolutional neural network Ginji Hirano, Mitsutaka Nemoto, Yuich Kimura, Takashi Nagaoka (Kindai University) MICT2020-20 MI2020-46 |
Melanoma is a type of superficial tumor, which is highly malignant. Early-stage melanoma is difficult to diagnose becaus... [more] | MICT2020-20 MI2020-46 pp.62-64 |
| NC, MBE (Joint) |
2019-03-06 09:55 |
Tokyo | University of Electro Communications | A study of inner feature continuity of the VGG model Toya Teramoto, Hyaru Shouno (UEC) NC2018-88 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classifi... [more] | NC2018-88 pp.239-244 |
| PRMU, CNR | 2019-02-28 14:15 |
Tokushima | Local Zoo Guide Application using Image Recognition Rabarison Misamanana Felicia, Kohei Kawanaka, Hiroki Tanioka, Tetsushi Ueta (Tokushima Univ.) PRMU2018-118 CNR2018-41 |
Most of the information for guide in the Tokushima zoo is in Japanese. However, it is difficult to understand Japanese g... [more] | PRMU2018-118 CNR2018-41 pp.21-26 |
|
| EST, MW, OPE, MWP, EMT, IEE-EMT, THz [detail] | 2018-07-19 15:20 |
Hokkaido | GPR Image Recognition by Transfer Learning with FDTD Simulation on Deep Learning Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) EMT2018-17 MW2018-32 OPE2018-20 EST2018-15 MWP2018-16 |
In this study, to automatically detect underground objects from the ground penetrating radar (GPR) images by the deep ne... [more] | EMT2018-17 MW2018-32 OPE2018-20 EST2018-15 MWP2018-16 pp.59-62 |
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