Abstract
In view of the low detection quality of traditional image detection system, which is prone to fuzzy edge details, An image-edge detection system based on supervised machine learning algorithm is proposed. The overall framework of image edge detection system was constructed through supervised machine learning calculation. Based on the framework of the system, images that cannot be classified linearly or approximately in the normal sample space are classified by the support vector machine (SVM) model, According to the fitting results, every pixel in the image, As a training sample, So that the two-dimensional image can be represented by the corresponding Lagrangian function, It can also represent linear combinations of nucleated functions, Finally, the image edge can be detected by zero cross detection. The experimental results show that the proposed system can detect the image edges well and the target details are clear.
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Acknowledgments
This study was supported by science and technology plan of Qinghai province Key Research &Development and conversion plan, Qinghai Province, Qinghai (No. 2019-GX-170).
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Li, K., Wang, H., Ouyang, L., Zhao, B. (2021). Image-Edge Detection System Based on Supervised Machine Learning Algorithm. In: Wang, X., Wong, KK., Chen, S., Liu, M. (eds) Artificial Intelligence for Communications and Networks. AICON 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-90199-8_24
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DOI: https://doi.org/10.1007/978-3-030-90199-8_24
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