The document describes a chessboard segmentation algorithm with the following steps:
1. Detect the borders of the chessboard using Hough transform or normalized cross correlation.
2. Split the board into squares by detecting dominant color values and iterating on x and y axes.
3. Classify each square as empty or containing a piece using standard deviation thresholds or a 40-dimensional feature space considering pixel ratios and piece border positions.
4. Produce a FEN notation for the detected board. The algorithm achieved accuracy from 55% to 100% on test images but requires further refinement, expanded training data, and optimized code.
19. Conclusion
• Chess Detection and Segmentation a
very hard problem in a general case
• We managed to solve a small part of it
• Each Step poses challenges
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Chess Segmentation
4/13/2012