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Content Provider | IEEE Xplore Digital Library |
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Author | Gee-Sern Hsu Hsiao-Chia Peng Shang-Min Yeh |
Copyright Year | 2012 |
Description | Author affiliation: Artificial Vision Lab., National Taiwan University of Science and Technology, Taipei, Taiwan (Gee-Sern Hsu; Hsiao-Chia Peng; Shang-Min Yeh) |
Abstract | 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. |
Starting Page | 857 |
Ending Page | 862 |
File Size | 1117559 |
Page Count | 6 |
File Format | |
ISBN | 9781467317139 |
e-ISBN | 9781467317146 |
e-ISBN | 9781467317122 |
DOI | 10.1109/ICSMC.2012.6377835 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-10-14 |
Publisher Place | Korea (South) |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Feature extraction Lighting Image color analysis Detectors Cameras Color Image edge detection local descriptor Object recognition foreground segmentation invariant feature |
Content Type | Text |
Resource Type | Article |
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