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1 Playing Chess: Recognition and Simulation COMP 290-075 Computer Vision B. Danette Allen Paul J. McLaurin May 03, 2000.

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Presentation on theme: "1 Playing Chess: Recognition and Simulation COMP 290-075 Computer Vision B. Danette Allen Paul J. McLaurin May 03, 2000."— Presentation transcript:

1 1 Playing Chess: Recognition and Simulation COMP Computer Vision B. Danette Allen Paul J. McLaurin May 03, 2000

2 2 Objectives Track individual chess piecesTrack individual chess pieces Maintain state of boardMaintain state of board Graphically represent state changes and stateGraphically represent state changes and state

3 3 Lab Set-up 1 to 3 cameras1 to 3 cameras –Panasonic GP-LM7TA –1 overhead (min) –2 side view PCPC Matrox imaging board and librariesMatrox imaging board and libraries C++, OpenGL, GLVUC++, OpenGL, GLVU Chessboard and piecesChessboard and pieces

4 4 System Constraints Single move per imageSingle move per image –move, capture, en passant, castle Shadow minimizationShadow minimization –currently using halogen lamp (no overhead) Not constrained to legal chess movesNot constrained to legal chess moves Must start game from beginningMust start game from beginning

5 5 Calibration of chess board

6 6 A blob is a set of connected pixels that have the same intensity valueA blob is a set of connected pixels that have the same intensity value There are five steps in this process:There are five steps in this process: 1. Acquire an image 2. Analyze foreground and background pixels to determine optimum threshold required to segment objects from their background 3. Apply a threshold to create a binary image with objects shown in white and background shown in black 4. Analyze areas of connected white pixels and assign a label or number to each discrete group of connected pixels 5. Extract physical measurements from objects. Blob Analysis

7 7 Grayscale and Binarized Images

8 8 Rendered chessboard and pieces

9 9 Problems Encountered Black on black not detectableBlack on black not detectable –white ring around base of black pieces –black ring around bases of white pieces Multiple responsesMultiple responses –Added code to filter out redundant responses Noisy difference imagesNoisy difference images –Gaussian filter

10 10 Future Enhancements Multiple/Side camerasMultiple/Side cameras Tracking of individual piecesTracking of individual pieces –“discrete” tracking of pose –“continuous” tracking (6 DOF) Handle shadowsHandle shadows Increase code robustnessIncrease code robustness –Verify legal moves in all cases »Handle “nudged” pieces automatically

11 11 Demonstrations Video of chess game in labVideo of chess game in lab Simulated chess gameSimulated chess game


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