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Geometry-Based Populated Chessboard Recognition

机译：基于几何的填充棋盘识别

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Chessboards are commonly used to calibrate cameras, and many robust methods have been developed to recognize the unoccupied boards. However, when the chessboard is populated with chess pieces, such as during an actual game, the problem of recognizing the board is much harder. Challenges include occlusion caused by the chess pieces, the presence of outlier lines and low viewing angles of the chessboard. In this paper, we present a novel approach to address the above challenges and recognize the chessboard. The Canny edge detector and Hough transform are used to capture all possible lines in the scene. The k-means clustering and a k-nearest-neighbors inspired algorithm are applied to cluster and reject the outlier lines based on their Euclidean distances to the nearest neighbors in a scaled Hough transform space. Finally, based on prior knowledge of the chessboard structure, a geometric constraint is used to find the correspondences between image lines and the lines on the chessboard through the homography transformation. The proposed algorithm works for a wide range of the operating angles and achieves high accuracy in experiments.
机译：棋盘通常用于校准相机，并且已经开发出许多可靠的方法来识别空闲的棋盘。但是，当在棋盘上填充棋子时，例如在实际游戏中，识别棋盘的问题就困难得多。挑战包括由棋子引起的遮挡，离群线的存在和棋盘的低视角。在本文中，我们提出了一种新颖的方法来应对上述挑战并认识棋盘。 Canny边缘检测器和Hough变换用于捕获场景中所有可能的线。将k均值聚类和k最近邻启发算法应用于聚类并基于缩放后的Hough变换空间中基于到最近邻的欧氏距离来排除离群线。最后，基于对棋盘结构的先验知识，通过单应性变换，使用几何约束来找到图像线与棋盘上的线之间的对应关系。所提出的算法适用于较大的工作角度范围，并在实验中达到了很高的精度。

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