Chess-Board-Recognition

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.