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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.


A data set and the clustering results.
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f5-sensors-08-01212: A data set and the clustering results.

Mentions: For example, considering a two-dimensional data set, as shown in Figure 5(a), MUGCS is used to classify the clusters for a data set with an initial distinguishing coefficient ξ= 0.1, an initial weighting coefficient ω = 0.2, the raising values Δω = 0.05 and Δξ = 0.1, and the expected cluster numbers EN = 2, 3, 4, 5, 6, 7 individually. The locations and the clustering results of data are indicated in Figures 5(b)-5(g), and the cluster centers are marked by the symbol “*”. The expected cluster number (EN), performance index (PI), and two optimal parameters ξ, ω, are given in Table 1. The value of PI varies while the cluster number is changing. Then, the optimal value of PI can be obtained. Thus, MUGCS can classify a data set to several clusters. Finally, MUGCA is executed to recognize the location and the spot number of each die (cluster) in this study.


An Auto-Recognizing System for Dice Games Using a Modified Unsupervised Grey Clustering Algorithm
A data set and the clustering results.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-08-01212: A data set and the clustering results.
Mentions: For example, considering a two-dimensional data set, as shown in Figure 5(a), MUGCS is used to classify the clusters for a data set with an initial distinguishing coefficient ξ= 0.1, an initial weighting coefficient ω = 0.2, the raising values Δω = 0.05 and Δξ = 0.1, and the expected cluster numbers EN = 2, 3, 4, 5, 6, 7 individually. The locations and the clustering results of data are indicated in Figures 5(b)-5(g), and the cluster centers are marked by the symbol “*”. The expected cluster number (EN), performance index (PI), and two optimal parameters ξ, ω, are given in Table 1. The value of PI varies while the cluster number is changing. Then, the optimal value of PI can be obtained. Thus, MUGCS can classify a data set to several clusters. Finally, MUGCA is executed to recognize the location and the spot number of each die (cluster) in this study.

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.