상세정보
학술대회
A method for converting an image of a GO game record to training data
- 저자
- 양혜리, 허현범, 정성욱, 이용우, 홍준영, 김예주, 이경재
- 발행일
- 202207
- 출처
- International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2022, pp.275-277
- 협약과제
- 22HH9200, 실·가상 환경 해석 기반 적응형 인터랙션 기술 개발, 정성욱
- 초록
- Supervised learning is an approach to training artificial intelligence using a labeled dataset. Since the interest in artificial intelligence is high, preparing training data for supervised learning has an important meaning. In this paper, we propose a feasible algorithm for converting a GO game dataset stored as an image into learnable labeled data. The training data need to have the location and color of GO stones. However, detecting a GO board is difficult because of the area covered by a GO stone. Furthermore, it is hard to know the reference point of the board, so recognizing the locations of the stone is also difficult. To detect a GO board, the proposed method is applied differently depending on whether or not there are GO stones on the board. Additionally, by finding the starting point of the GO board, we can convert the location of the GO stone in the image to training data.
- KSP 제안 키워드
- Game dataset, Labeled data, Reference point, Starting point, Supervised Learning, artificial intelligence, training data