Probabilistic location of a populated chessboard using computer vision

Development of autonomic chess-playing robots creates several interesting computer vision problems, including plane calibration and object recognition. Various solutions have been attempted, but most either require a modified chess set or place unreasonable constraints on board conditions and camera angles. A more general solution uses computer vision to automatically determine arbitrary chessboard location and identify chessmen on a standard, unmodified chess set. Although much work has been devoted to probabilistic image recognition in general, this paper presents a novel solution to the specific chessboard location problem that is accurate, less restrictive, and relatively time efficient.