Volume 14 Number 8 (Aug. 2019)
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JCP 2019 Vol.14(8): 519-527 ISSN: 1796-203X
doi: 10.17706/jcp.14.8.519-527

Towards Computer-Vision-Based Learning from Demonstration (CVLfD): Chess Piece Recognition

Regina Wolff, Anoshan Indreswaran, Matthias Krauledat, Ronny Hartanto
Rhine-Waal University of Applied Sciences, Faculty of Technology and Bionics, Kleve 47533, Germany.
Abstract—We present an approach to develop algorithms to offer ‘Learning from Demonstration’. Our aim is to realize Computer Vision as resource-efficient as possible in applications where people interact with computers or -as a special case- with robots. This paper explains the development of a classification program which is to be integrated to a robot that will autonomously play chess. The problem is to perform a classification on a 12 class data set of chess pieces which works on a real-time video feed. We develop two different approaches to solve the problem: A one-step classification is compared to a two-step procedure based on accuracy, computational time and robustness.

Index Terms—Chess, computer vision, convolutional neuronal network.

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Cite: Regina Wolff, Anoshan Indreswaran, Matthias Krauledat, Ronny Hartanto, "Towards Computer-Vision-Based Learning from Demonstration (CVLfD): Chess Piece Recognition," Journal of Computers vol. 14, no. 8, pp. 519-527, 2019.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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