Chess Recognition
Chess recognition is a fun topic that I often used for examples in my computer vision class. The idea is to determine the identity and positions of all chess pieces on the board, from a single image. I did this project with a graduate student, Youye Xie.
Board Detection
The first step in a complete chess recognition system is to detect the chess board, and determine its pose. Although chess boards are widely used for camera calibration, this is NOT what we are doing! Calibration boards are much easier to detect and there are off-the-shelf algorithms to detect the board. When the chessboard is populated with chess pieces, such as during an actual game, the problem of recognizing the board is much harder. Challenges include occlusion caused by the chess pieces, the presence of outlier lines and low viewing angles of the chessboard.
Chess boards are widely used for camera calibration, but this is not what we are addressing in this project.
We developed a novel approach, based on clustering in a Hough transform space, to find the lines on the chessboard. The algorithm works for a wide range of the operating angles and achieves high accuracy in experiments. Full details are given in this paper: Y. Xie, G. Tang, W. Hoff, "Geometry-Based Populated Chessboard Recognition." International Conference on Machine Vision (ICMV), November 2017, Vienna, Austria. (pdf)
Piece Recognition
Once the board is detected, it is easier to recognize the pieces. We developed an efficient 3D piece recognition approach based on oriented chamfer matching. During a real game, the pieces might be occluded by other pieces and have varying rotation and scales with respect to the camera. Furthermore, different pieces share lots of similar texture features which makes them more difficult to identify.
Our approach addresses the above problems and is capable of identifying the pieces with different scales, rotation and viewing angles. After marking the possible chessboard squares that contain pieces, the oriented chamfer scores are calculated for alternative templates and the recognized pieces are indicated on the input image accordingly.
Our approach shows high recognition accuracy and efficiency in experiments and the recognition process can be easily generalized to other pattern recognition applications with 3D templates. Our approach outperforms convolutional neural networks under severe occlusion and low resolution conditions and has comparative processing time while avoiding a time consuming training process.
For full details, see the paper: Y. Xie, G. Tang, W. Hoff, "Chess Piece Recognition Using Oriented Chamfer Matching with a Comparison to CNN ." Proc. of Winter Conference on Applications of Computer Vision (WACV), March 2018, Lake Tahoe, Nevada. (pdf)