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An Auto-Recognizing System for Dice Games Using a Modified Unsupervised Grey Clustering Algorithm

View Article: PubMed Central - PubMed

ABSTRACT

In this paper, a novel identification method based on a machine vision system is proposed to recognize the score of dice. The system employs image processing techniques, and the modified unsupervised grey clustering algorithm (MUGCA) to estimate the location of each die and identify the spot number accurately and effectively. The proposed algorithms are substituted for manual recognition. From the experimental results, it is found that this system is excellent due to its good capabilities which include flexibility, high speed, and high accuracy.

No MeSH data available.


Abreast dice.
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f7-sensors-08-01212: Abreast dice.

Mentions: In general, the dices are undistinguishable when they are abreast in a bowl. Three types of four abreast dice are shown in Figure 7. However, the ARDS can easily separate abreast dice as illustrated in Figure 8. Furthermore, the recognizing ability of ARDS is independent of the image size of the dice. The locations of dice in Figure 9(a) and 9(b) are the same, but the acquired images are different in the size of distances between the dices for the different position of the CCD camera. The same classification results are obtained using ARDS software, as shown in Figures 9(c) and 9(d).


An Auto-Recognizing System for Dice Games Using a Modified Unsupervised Grey Clustering Algorithm
Abreast dice.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927534&req=5

f7-sensors-08-01212: Abreast dice.
Mentions: In general, the dices are undistinguishable when they are abreast in a bowl. Three types of four abreast dice are shown in Figure 7. However, the ARDS can easily separate abreast dice as illustrated in Figure 8. Furthermore, the recognizing ability of ARDS is independent of the image size of the dice. The locations of dice in Figure 9(a) and 9(b) are the same, but the acquired images are different in the size of distances between the dices for the different position of the CCD camera. The same classification results are obtained using ARDS software, as shown in Figures 9(c) and 9(d).

View Article: PubMed Central - PubMed

ABSTRACT

In this paper, a novel identification method based on a machine vision system is proposed to recognize the score of dice. The system employs image processing techniques, and the modified unsupervised grey clustering algorithm (MUGCA) to estimate the location of each die and identify the spot number accurately and effectively. The proposed algorithms are substituted for manual recognition. From the experimental results, it is found that this system is excellent due to its good capabilities which include flexibility, high speed, and high accuracy.

No MeSH data available.