{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:18:03Z","timestamp":1781194683609,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,2,8]],"date-time":"2019-02-08T00:00:00Z","timestamp":1549584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>As we move towards improving the skill of computers to play games like chess against humans, the ability to accurately perceive real-world game boards and game states remains a challenge in many cases, hindering the development of game-playing robots. In this paper, we present a computer vision algorithm developed as part of a chess robot project that detects the chess board, squares, and piece positions in relatively unconstrained environments. Dynamically responding to lighting changes in the environment, accounting for perspective distortion, and using accurate detection methodologies results in a simple but robust algorithm that succeeds 100% of the time in standard environments, and 80% of the time in extreme environments with external lighting. The key contributions of this paper are a dynamic approach to the Hough line transform, and a hybrid edge and morphology-based approach for object\/occupancy detection, that enable the development of a robot chess player that relies solely on the camera for sensory input.<\/jats:p>","DOI":"10.3390\/computers8010014","type":"journal-article","created":{"date-parts":[[2019,2,11]],"date-time":"2019-02-11T03:26:01Z","timestamp":1549855561000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Robust Computer Vision Chess Analysis and Interaction with a Humanoid Robot"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6239-8443","authenticated-orcid":false,"given":"Andrew","family":"Chen","sequence":"first","affiliation":[{"name":"Embedded Systems Research Group, Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Wang","sequence":"additional","affiliation":[{"name":"Embedded Systems Research Group, Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1080\/14786445008521796","article-title":"Programming a Computer for Playing Chess","volume":"41","author":"Shannon","year":"1950","journal-title":"Philos. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, A.T.-Y., and Wang, K.I.-K. (2016, January 28\u201330). Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot. Proceedings of the International Conference on Control, Automation and Robotics (ICCAR), Hong Kong, China.","DOI":"10.1109\/ICCAR.2016.7486689"},{"key":"ref_3","unstructured":"Mukhamedov, E. (2016, January 14). Chesska defends World Champion title in Robot Chess. Available online: http:\/\/en.chessbase.com\/post\/cheka-defends-world-champion-title-in-robot-che."},{"key":"ref_4","unstructured":"Danner, C., and Kafafy, M. (2019, February 07). Visual Chess Recognition, 2015. Available online: http:\/\/web.stanford.edu\/class\/ee368\/Project_Spring_1415\/Reports\/Danner_Kafafy.pdf."},{"key":"ref_5","unstructured":"Urting, D., and Berbers, Y. (2003, January 25\u201327). MarineBlue: A Low-cost Chess Robot. Proceedings of the IASTED International Conference on Robotics and Applications, Salzburg, Austria."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sokic, E., and Ahic-Djokic, M. (2008, January 16\u201319). Simple Computer Vision System for Chess Playing Robot Manipulator as a Project-based Learning Example. Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/ISSPIT.2008.4775676"},{"key":"ref_7","unstructured":"Hack, J., and Ramakrishnan, P. (2019, February 07). CVChess: Computer Vision Chess Analytics, 2014. Available online: http:\/\/cvgl.stanford.edu\/teaching\/cs231a_winter1415\/prev\/projects\/chess.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Neufeld, J.E., and Hall, T.S. (2010, January 1\u20134). Probabilistic Location of a Populated Chessboard using Computer Vision. Proceedings of the IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Seattle, WC, USA.","DOI":"10.1109\/MWSCAS.2010.5548901"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tam, K.Y., Lay, J.A., and Levy, D. (2008, January 1\u20133). Automatic Grid Segmentation of Populated Chessboard Taken at a Lower Angle View. Proceedings of the Digital Image Computing: Techniques and Applications (DICTA), Canberra, Australia.","DOI":"10.1109\/DICTA.2008.40"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.cviu.2013.10.008","article-title":"ChESS \u2013 Quick and Robust Detection of Chess-board Features","volume":"118","author":"Bennett","year":"2014","journal-title":"Computer Vis. Image Underst."},{"key":"ref_11","unstructured":"Czyzewski, M.A. (arXiv, 2017). An Extremely Efficient Chess-board Detection for Non-trivial Photos, arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Matuszek, C., Mayton, B., Aimi, R., Deisenroth, M.P., Bo, L., Chu, R., Kung, M., LeGrand, L., Smith, J.R., and Fox, D. (2011, January 9\u201313). Gambit: An Autonomous Chess-playing Robotic System. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980528"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luqman, H.M., and Zaffar, M. (2016, January 4\u20136). Chess Brain and Autonomous Chess Playing Robotic System. Proceedings of the International Conference on Autonomous Robot Systems and Competitions (ICARSC), Bragan\u00e7a, Portugal.","DOI":"10.1109\/ICARSC.2016.27"},{"key":"ref_14","unstructured":"Cour, T., Lauranson, R., and Vachette, M. (2019, February 07). Autonomous Chess-Playing Robot, 2002. Available online: https:\/\/pdfs.semanticscholar.org\/57e7\/9b85d53597d59a1009ea964876de260935ea.pdf."},{"key":"ref_15","unstructured":"Bradski, G. (2019, February 07). The OpenCV Library. Available online: https:\/\/opencv.org\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0734-189X(88)80033-1","article-title":"A Survey of the Hough Transform","volume":"44","author":"Illingworth","year":"1988","journal-title":"Computer Vis. Gr. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TSMCB.2006.886952","article-title":"On Learning, Representing, and Generalizing a Task in a Humanoid Robot","volume":"37","author":"Calinon","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_19","unstructured":"Pereira, A., Martinho, C., Leite, I., and Paiva, A. (2008, January 12\u201316). iCat, the Chess Player: The Influence of Embodiment in the Enjoyment of a Game. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, Estoril, Portugal."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karpouzis, K., and Yannakakis, G.N. (2016). Games Robots Play: Once More, with Feeling. Emotion in Games: Theory and Praxis, Springer.","DOI":"10.1007\/978-3-319-41316-7"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schwenk, A., and Yuan, C. (2015, January 14\u201316). Visual Perception and Analysis as First Steps Toward Human-Robot Chess Playing. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-27863-6_26"},{"key":"ref_23","unstructured":"Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., and Graepel, T. (arXiv, 2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/8\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:42Z","timestamp":1760185842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/8\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,8]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["computers8010014"],"URL":"https:\/\/doi.org\/10.3390\/computers8010014","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,8]]}}}