Color and illumination invariant dice recognition

• Published in: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 857 - 862
• Main Authors: Gee-Sern Hsu, Hsiao-Chia Peng, Shang-Min Yeh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Cameras; Color; Detectors; Feature extraction; foreground segmentation; Image color analysis; Image edge detection; invariant feature; Lighting; local descriptor; Object recognition
• ISBN: 9781467317139; 1467317136
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/ICSMC.2012.6377835

A system is proposed for automatic reading of the number of dots on dice in general table game settings. Different from previous dice recognition systems which recognize dice of a specific color using a single top-view camera in an enclosure with controlled settings, the proposed one uses multiple cameras to recognize dice of various colors posed in a wide range of viewing angle and under uncontrolled conditions. It is composed of three modules. Module-1 locates the dice using the gradient-conditioned color segmentation (GCCS), proposed in this paper, to segment dice of arbitrary colors from the background. Module-2 exploits the local invariant features good for building homographies across multiple views and lighting conditions. The homographies are used to enhance coplanar features and weaken non-coplanar features, giving a solution to segment the top faces of the dice and make up the features ruined by possible specular reflection. To identify the dots on the segmented top faces, an MSER detector is embedded in Module-3 for its consistency in locating the dot regions regardless of illumination and viewpoint variations. Experiments show that the proposed system performs satisfactorily in various test conditions.