Limits...
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.


The classification result when threshold value is 90
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3927534&req=5

f10-sensors-08-01212: The classification result when threshold value is 90

Mentions: One hundred sample sets including 3, 4, 5, and 6 dice were randomly sampled from acquired image in order to test ARDS software. After all, an accuracy rate 100% can be achieved when threshold value setting is between 100 and 150 in binary process under a controllable environment. For example, detection results were 4, 5, 5, 3 instead of 2, 5, 5, 3 when threshold value is 90 as shown in Figure 10. The average of iteration number is 21 after testing by ARDS software. Table 2 presents the classification results of three samples. Even for low contrast images, acceptable results can still be obtained (Figure 11).


An Auto-Recognizing System for Dice Games Using a Modified Unsupervised Grey Clustering Algorithm
The classification result when threshold value is 90
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-08-01212: The classification result when threshold value is 90
Mentions: One hundred sample sets including 3, 4, 5, and 6 dice were randomly sampled from acquired image in order to test ARDS software. After all, an accuracy rate 100% can be achieved when threshold value setting is between 100 and 150 in binary process under a controllable environment. For example, detection results were 4, 5, 5, 3 instead of 2, 5, 5, 3 when threshold value is 90 as shown in Figure 10. The average of iteration number is 21 after testing by ARDS software. Table 2 presents the classification results of three samples. Even for low contrast images, acceptable results can still be obtained (Figure 11).

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.