Mounted PCB Classification System Using Wavelet and ART2 Neural Network 


Vol. 6,  No. 5, pp. 1296-1302, May  1999
10.3745/KIPSTE.1999.6.5.1296


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  Abstract

In this paper, we propose an algorithm for the mounted PCB classification system using wavelet transform and ART2 neural network. The feature informations of mounted PCB can be extracted from the coefficient matrix of wavelet transform adapted subband concept. As the preprocessing process, only the PCB area in the input image is extracted by histogram method and the feature vectors are composed of using wavelet transform method. These feature vectors are used as the input vector of ART2 neural network. In the experiment using 55 mounted PCB images, the proposed algorithm shows 100% classification rate at the vigilance parameter %u03C1=0.99. The proposed algorithm has some advantages of the feature extraction in the compressed domain and the simplification of processing steps.

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[IEEE Style]

K. S. Cheol and J. S. Hwan, "Mounted PCB Classification System Using Wavelet and ART2 Neural Network," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 5, pp. 1296-1302, 1999. DOI: 10.3745/KIPSTE.1999.6.5.1296.

[ACM Style]

Kim Sang Cheol and Jung Sung Hwan. 1999. Mounted PCB Classification System Using Wavelet and ART2 Neural Network. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 5, (1999), 1296-1302. DOI: 10.3745/KIPSTE.1999.6.5.1296.

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