This project highlights approaches taken to process an image of a chessboard and identify the configuration of the board using computer vision techniques. Although, the use of a chessboard detection for camera calibration is a classic vision problem, existing techniques on piece recognition work under a controlled environment. The procedures are customized for a chosen colored chessboard and a particular set of pieces. The methods used in this project supplements existing research by using clustering to segment the chessboard and pieces irrespective of color schemes. For piece recognition, the method introduces a novel approach of using a R-CNN to train a robust classifier to work on different kinds of chessboard pieces. The method performs better on different kinds of pieces as compared to a SIFT based classifier. If extended, this work could be useful in recording moves and training chess AI for predicting the best possible move for a particular chessboard configuration.
Approach Stack:
Clusters Obtained:
Detected Lines
Pieces extracted
Recognition:
NOTE: For more details refer to the report.