DocumentCode :
1576545
Title :
Probabilistic location of a populated chessboard using computer vision
Author :
Neufeld, Jason E. ; Hall, Tyson S.
Author_Institution :
Sch. of Comput., Southern Adventist Univ., Collegedale, TN, USA
fYear :
2010
Firstpage :
616
Lastpage :
619
Abstract :
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.
Keywords :
image recognition; intelligent robots; object recognition; robot vision; chess playing robots; computer vision; image recognition; object recognition; plane calibration; populated chessboard; probabilistic location; Calibration; Cameras; Computer vision; Educational institutions; Humans; Image recognition; Machine vision; Object recognition; Robot vision systems; Robotics and automation; Chess; Games; Machine vision; Object recognition; Robot vision systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems (MWSCAS), 2010 53rd IEEE International Midwest Symposium on
Conference_Location :
Seattle, WA
ISSN :
1548-3746
Print_ISBN :
978-1-4244-7771-5
Type :
conf
DOI :
10.1109/MWSCAS.2010.5548901
Filename :
5548901
Link To Document :
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