Techniques of Image Processing Based on Artificial Neural Networks  

作　　者：李伟青 王群 王成彪 

机构地区：[1]School of Engineering and Technology,China University of Geosciences [2]School of Information and Technology,China University of Geosciences

出　　处：《Journal of Donghua University(English Edition)》2006年第6期20-24,共5页东华大学学报（英文版）

基　　金：Supported by Science and Technology Fundation (China University of Geosciences) (No.200520)

摘　　要：This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

关 键 词：neural networks backpropagation networks Chromatism classification edge detection image processing. 

分 类 号：Q189[生物学—神经生物学]