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Bara Lilla

Team H : Automatic Poker Player. Nyíri Gergely. Bara Lilla. Piotr Czeka ń ski. Kovács Laura. Automatic Poker Player. Usage. General Presentation. Detailed Presentation. Conclusion and Further Work. Usage. Determine the shapes of poker-cards (i.e. the hand value) Difficulties:

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Bara Lilla

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  1. Team H: Automatic Poker Player Nyíri Gergely Bara Lilla Piotr Czekański Kovács Laura

  2. Automatic Poker Player Usage General Presentation Detailed Presentation Conclusion and Further Work

  3. Usage • Determine the shapes of poker-cards • (i.e. the hand value) • Difficulties: • Hidden parts of cards • Cards in different positions (angles) • Motivation: Electronic Casinos

  4. General Presentation Corners of the cards Image Processing - method 1 - Existing symbols Image Processing - method 2 - Pattern recognition Shape of the Cards

  5. Detailed Presentation • Image Processing – method 1 • Image Processing – method 2 • Pattern recognition

  6. D heart, D spade, D diamond, D club Image Processing – method 1 • Step 1: Tresholding using isodata algorithm • Step 2: Fill area, closing holes • Step 3: Determine the boundary • Step 4: Labeling the boundaries • Step 5: Compute the chain code • Step 6: Determine the corners • Step 7: Determine the angle • Step 8: Rotate the cards • Step 9: Pattern matching

  7. Image Processing – method 2 • Localize the cards • Keep only the symbols • Extracting the relevant symbols – from distance analysis • Pattern recognition – numbers and symbols

  8. Pattern Matching

  9. Conclusions • Results • Further Work

  10. Results • Detecting the shape of poker cards - even if some parts are hidden • The performances of the algorithms depend on the numbers of the contained symbols

  11. Problems

  12. Using last year’s Dice Project implementing an SSIP Casino Further Work • Analyze the information of the shapes => determine the hand value of the poker cards • Improve the algorithms by using fuzzy logic or neural networks

  13. Thank You for the Attention!

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