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Abstrakty
Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations. We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization. The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the subprocess of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).
Rocznik
Tom
Strony
257--280
Opis fizyczny
Bibliogr. 47 poz., rys., tab.
Twórcy
autor
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, Poland
autor
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, Poland
- European Center for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, Poznan, Poland
autor
- European Center for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, Poznan, Poland
Bibliografia
- [1] Acher M. and Esnault F. Large-scale analysis of chess games with chess engines: Apreliminary report, 2016.
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- [3] Bency A.J., Kwon H., Lee H., Karthikeyan S., and Manjunath B. Weakly supervised localization using deep feature maps. In European Conference on Computer Vision, pages 714-731, Springer, 2016.
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- [7] CoolThings. Square off is a robot chess board that can move pieces on its own, November 2016.
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- [9] Czyzewski M.A., Laskowski A., and Wasik S. Latchess 21: dataset of damaged chessboard lattice points (chessboard features) used to train laps detector (grayscale/21x21px), 2018.
- [10] Danner C. and Kafafy M. Visual chess recognition, 2015.
- [11] De la Escalera A. and Armingol J.M. Automatic chessboard detection for intrinsic andextrinsic camera parameter calibration.Sensors, 10(3):2027–2044, 2010.
- [12] Ding J. Chessvision: Chess board and piece recognition. Technical report, Stanford University, 2016.
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- [38] Tam K.Y., Lay J.A., and Levy D. Automatic grid segmentation of populated chess-board taken at a lower angle view. In Computing: Techniques and Applications, 2008.DICTA’08. Digital Image, pages 294-299. IEEE, 2008.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8de9dbe6-cad3-443b-b761-13219dc3325c