| Techniques of Image Processing Based on Artificial Neural Networks |
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| 作者姓名： | 李伟青 王群 王成彪 |
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| 作者单位： | [1]School of Engineering and Technology, China University of Geosciences, Beijing 100083 [2]School of Information and Technology, China University of Geosciences, Beijing 100083 |
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| 基金项目： | Supported by Science and Technology Foundation （China University of Geosciences） （No. 200520） |
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| 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.
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| 关 键 词： | 人造神经网络 图像处理技术 色差 分级 |
| 收稿时间： | 2006-08-20 |
Techniques of Image Processing Based on Artificial Neural Networks |
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| LI Wei-qing,WANG Qun,WANG Cheng-biao.Techniques of Image Processing Based on Artificial Neural Networks[J].Journal of Donghua University,2006,23(6):20-24. |
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| Authors: | LI Wei-qing WANG Qun WANG Cheng-biao |
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| Institution: | 1. School of Engineering and Technology, China University of Geosciences, Beijing 100083
2. School of Information and Technology, China University of Geosciences, Beijing 100083 |
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| 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. |
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| Keywords: | neural networks backpropagation networks Chromatism classification edge detection image processing |
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