东华大学学报（英文版）2006，Vol.23Issue(6)：20-24,5.
Techniques of Image Processing Based on Artificial Neural Networks
Techniques of Image Processing Based on Artificial Neural Networks
摘要
Abstract
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 processingKey words
neural networks/backpropagation networks/Chromatism classification/edge detection/image processing分类
信息技术与安全科学引用本文复制引用
LI Wei-qing,WANG Qun,WANG Cheng-biao..Techniques of Image Processing Based on Artificial Neural Networks[J].东华大学学报（英文版）,2006,23(6):20-24,5.基金项目
Supported by Science and Technology Foundation (China University of Geosciences) (No. 200520) （China University of Geosciences）
