Virtual Chess - Computer Science

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

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

Lab Set-up 1 to 3 cameras Panasonic GP-LM7TA 1 overhead (min) 2 side view PC Matrox imaging board and libraries C++, OpenGL, GLVU

Chessboard and pieces 3 System Constraints Single move per image move, capture, en passant, castle Shadow minimization currently using halogen lamp (no overhead)

Not constrained to legal chess moves Must start game from beginning 4 Calibration of chess board 5 Blob Analysis

A blob is a set of connected pixels that have the same intensity value 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. 6 Grayscale and Binarized Images 7 Rendered chessboard and

pieces 8 Problems Encountered Black on black not detectable white ring around base of black pieces black ring around bases of white pieces Multiple responses

Added code to filter out redundant responses Noisy difference images Gaussian filter 9 Future Enhancements Multiple/Side cameras Tracking of individual pieces discrete tracking of pose

continuous tracking (6 DOF) Handle shadows Increase code robustness Verify legal moves in all cases Handle nudged pieces automatically 10 Demonstrations

Video of chess game in lab Simulated chess game 11