PDF] ChessVision : Chess Board and Piece Recognition
Por um escritor misterioso
Last updated 20 setembro 2024
The method presented in this paper improves upon the segmentation-based approaches of previous work in piece recognition by introducing a novel approach using classifiers trained on feature descriptors, which is more robust to the similarities in color seen in real-life chess boards. This paper details a method to take an image of a chess board and output a reconstructed computer representation of the board through board and piece recognition. Though techniques for board recognition have been thoroughly explored in the past, especially in relation to chessboard calibration, previous works on piece recognition often focus on non-robust segmentation procedures that rely heavily on the exact color of customized chess boards and pieces. The method presented in this paper improves upon the segmentation-based approaches of previous work in piece recognition by introducing a novel approach using classifiers trained on feature descriptors, which is more robust to the similarities in color seen in real-life chess boards. This work is important for both automating the recording of moves in human chess games and improving the ability of chess-playing AI that depend on vision.
Improving Calculation and Visualization by playing one or more move behind. Any Chess Software or Server? : r/chess
J. Imaging, Free Full-Text
PlayChessWithAWebCam/Papers - BITPlan Wiki
PDF] A Computer Vision System for Chess Game Tracking
PDF] ChessVision : Chess Board and Piece Recognition
How to Get Better at Chess Tactics –
Search results for: 'chess' - Internet Chess Club
How to get better at chess tactics –
ChessVision-finalpaper
PDF] Chess Piece Recognition Using Oriented Chamfer Matching with a Comparison to CNN
PDF) Chessboard and chess piece recognition with the support of neural networks
Chess Recognition — William Hoff
GitHub - Rizo-R/chess-cv: Computer vision project to recognize chess positions
Board Game Image Recognition using Neural Networks, by Andrew Underwood
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