| Presentation | 2019-10-18 Recognition of a Shogi phase image taken with a smartphone Akira Sato, Kei Morizumi, Ikuko Shimizu, Masaki Nakagawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper reports recognition of Shogi phase images captured by a smartphone and transferred to our server. In these days, Shogi is played by a lot of people and some “Shogi AI” are available for evaluating Shogi phases. In order to use them, however, the users must input configurations of pieces in a Shogi phase to them, which takes time and effort. Our system is aimed to solve this problem. It has three parts: the detector of a Shogi board, the recognizer of Shogi pieces on the board and the recognizer of Shogi pieces beside the board. This system achieves 90% detection of the board by LSD, 99% recognition of pieces on a board by CNN, 73% precision of pieces and 97% recall of pieces beside the board by YOLOv3. We are availing this system for free. There remains work to improve the performance through accumulating data and analyzing problems revealed. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Machine Learning / Convolutional Neural Network / Shogi |
| Paper # | PRMU2019-33 |
| Date of Issue | 2019-10-11 (PRMU) |
| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2019/10/18(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yoichi Sato(Univ. of Tokyo) |
| Vice Chair | Toru Tamaki(Hiroshima Univ.) / Akisato Kimura(NTT) |
| Secretary | Toru Tamaki(NTT) / Akisato Kimura(OMRON SINICX) |
| Assistant | Yusuke Uchida(DeNA) / Takayoshi Yamashita(Chubu Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Recognition of a Shogi phase image taken with a smartphone |
| Sub Title (in English) | |
| Keyword(1) | Machine Learning |
| Keyword(2) | Convolutional Neural Network |
| Keyword(3) | Shogi |
| 1st Author's Name | Akira Sato |
| 1st Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 2nd Author's Name | Kei Morizumi |
| 2nd Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 3rd Author's Name | Ikuko Shimizu |
| 3rd Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 4th Author's Name | Masaki Nakagawa |
| 4th Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| Date | 2019-10-18 |
| Paper # | PRMU2019-33 |
| Volume (vol) | vol.119 |
| Number (no) | PRMU-235 |
| Page | pp.pp.11-15(PRMU), |
| #Pages | 5 |
| Date of Issue | 2019-10-11 (PRMU) |