Anglo-American Playing Cards Identification Based on Counting Each Symbols and Scale Invariant Feature Transform (SIFT)

Authors

  • Nancy Velasco E Departamento de Ciencias Exactas, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • Dario Jose Mendoza Chipantasi Departamento de Energía y Mecánica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • Victor H. Andaluz Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • Sergio Dominguez Escuela Técnica Superior de Ingenieros Industriales - Universidad Politécnica de Madrid.

Keywords:

Image Processing, Playing Card Detection, SIFT,

Abstract

This paper is inspired by detection and identification of Anglo-American playing cards from an image using an entry-level webcam and computer vision. Some authors have been made algorithms for playing card recognition, but the solution of playing card detection and recognition is still in progress. Although one could think of methods, which classify using the upper left and lower right corners of the card where the numbers are, this paper focuses on the novel method. Without reading the card code, we counted each individual symbols in the playing card. We implemented this algorithm in Matlab solving two parts: Locating the card on the image obtained in a controlled environment, and recognizing the cards. The recognition of playing cards was achieved through thresholding combined with color segmentation and the counting of the symbols in playing cards from one to ten and using SIFT for another playing cards.

References

Brinks Dan and White Hugh. 2007. Texas Hold'em Hand Recognition and Analysis.

Martins, P., Reis, L. P., & Teófilo, L. 2011. Poker vision: Playing Cards and Chips Identification Based on image processing. In Pattern Recognition and Image Analysis Springer Berlin Heidelberg. 436-443.

Chen, W. Y., & Chung, C. H. 2010. Robust Poker Image Recognition Scheme in Playing Card Machine using Hotelling Transform, DCT and Run-length Techniques. Digital Signal Processing, 20(3):769-779.

Kim, M. G., & Suzuki, K. 2012. A Card-playing Humanoid Playmate for Human Behavioral Analysis. Entertainment Computing, 3(4):103-109.

Zheng, Chunhui; Green, Richard. 2007. Playing Card Recognition using Rotational Invariant Template Matching. En Proc of Image and Vision Computing New Zealand, 276-281.

Zaworka, Arno; Scherer, Stefan. 2001. Machine Vision Driven RealTime Black Jack Analysis.

Hollinger, G., Ward, N., & Everbach, E. C. (2003). Introducing Computers to Blackjack: Implementation of a Card Recognition System using Computer Vision Techniques. Colby College, Waterville.

Cooper, Wesley; Dawson-Howe, Kenneth. 2004. Automatic blackjack monitoring. En Proc. of Irish Machine Vision Conference. 248-254.

Zutis K., & Hoey, J. 2009. Who’s counting? Real-time Blackjack Monitoring for Card Counting Detection. In Computer Vision Systems Springer Berlin Heidelberg. 354-363

Kool, Wouter. 2013. Analytics, Master Business. Automation of the SET Card Game.

Lowe, D G. 1999. Object Recognition from local Scale-Invariant Features. The Proceedings of the Seventh IEEE International Conference In Computer Vision. 1150-1157.

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Published

2017-04-01

How to Cite

E, N. V., Mendoza Chipantasi, D. J., Andaluz, V. H., & Dominguez, S. (2017). Anglo-American Playing Cards Identification Based on Counting Each Symbols and Scale Invariant Feature Transform (SIFT). Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 93–96. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1842