Mounted PCB Classification System Using Wavelet and ART2 Neural Network

웨이브렛과 ART2 신경망을 이용한 실장 PCB 분류 시스템

  • 김상철 (창원대학교 대학원 전자계산학과) ;
  • 정성환 (창원대학교 전자계산학과)
  • Published : 1999.05.01

Abstract

In this paper, we propose an algorithms for the mounted PCB classification system using wavelet transform and ART2 neural network. The feature informations of a 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 $\rho$=0.99. The proposed algorithm has some advantages of the feature extraction in the compressed domain and the simplification of processing steps.

Keywords