• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.567-570
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• 2010

In this paper, A Detection Algorithm of Dead Stone for the Go program based on the Grouping is proposed. The group of the same color as the stone is connected to the left, right, up and down side in the same group is defined to be grouping. A Detection Algorithm of Dead Stone based on these groups to remove stone and all the stones of the same color as the stones of the same group, regardless of the Case for TILT blocked, if satisfied that the detection of the group is treated as a dead stone.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.829-837
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• 2014

This paper develops an automatic go recording system based on image processing. We use Random Sample Consensus to detect the circular shape of stone and propose a set of methods to improve the computational overhead of RANSAC. The proposed scheme is not affected by the changes of stone location, illumination, and camera distance, which is different from existing methods. We implemented the proposed scheme into a working system and confirmed that the recording is feasible and the problems have been improved.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.724-733
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• 2017

Conventional methods of automatically recording Go game positions do not properly consider obstacles (hand or object) on a Go board during the Go game. If the Go board is blocked by obstacles, the position of a Go stone may not be correctly recognized, or the sequences of moves may be stored differently from the actual one. In the proposed algorithm, only the complete Go board image without obstacles is stored as a background image and the obstacle is recognized by comparing the background image with the current input image. To eliminate the phenomenon that the shadow is mistaken as obstacles, this paper proposes the new obstacle detection method based on the gradient image instead of the simple differential image. When there is no obstacle on the Go board, the background image is updated. Finally, the successive background images are compared to recognize the position and type of the Go stone. Experimental results show that the proposed algorithm has more than 95% recognition rate in general illumination environment.