This project highlights approaches taken to processan image of a chessboard and identify the configuration of theboard using computer vision techniques. Although, the use of achessboard detection for camera calibration is a classic visionproblem, existing techniques on piece recognition work undera controlled environment. The procedures are customized fora chosen colored chessboard and a particular set of pieces.The methods used in this project supplements existingresearch by using clustering to segment the chessboard andpieces irrespective of color schemes. For piece recognition, themethod introduces a novel approach of using a R-CNN to traina robust classifier to work on different kinds of chessboardpieces. The method performs better on different kinds of piecesas compared to a SIFT based classifier. If extended, this workcould be useful in recording moves and training chess AI forpredicting the best possible move for a particular chessboardconfiguration. Ged testing locations near me.
Coordinates, and (2) a model of chessboard colors and occlusions that. It is also notable that the OpenCV function. Of a Harris Corner Detector with a SIFT. All the signi cant functions in the VLFeat library are C callable because that’s how MATLAB calls them. The web site has pretty good documentation. This might be an easier way for C folks to go than the OpenCV route below only because OpenCV has moved away from SIFT because of legal reasons.
Approach Stack:
Clusters Obtained:
Detected Lines
Pieces extracted
Recognition:
Lionel train 2332 manuals. NOTE: For more details refer to the report.
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