Abstract
With an improved algorithm based on the Otsu method, this paper proposes solving the shadow region problem caused by non-uniform illumination. The recent image processing algorithms for handling the shaded area’s issue for the massive difference in the image grey value will not be practical. It is not easy to directly set the shaded area’s content to “black” based on this problem. This paper proposes improved threshold image segmentation for Non-uniform illumination based on the Otsu optimization Approach. It uses the local area threshold and window image pixel by sliding to enhance the image recognition rate of a shaded area. Experimental results verify that the proposed segmentation method has more robust adaptability to images with shadows. Compared with the classical global OTSU algorithm and genetic algorithm optimization, the image recognition rate is 96%.
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References
Guo, Z., Li, J., Zhao, Y., et al.: A survey of wireless sensor network technology in the Internet of Things. Comput. Appl. Chem. 36(01), 72–83 (2019)
Chen, T., Wang, Y., Xiao, C., Wu, Q.M.J.: A machine vision apparatus and method for can-end inspection. IEEE Trans. Instrum. Meas. 65(9), 2055–2066 (2016)
Szydlowski, M., Powalka, B., Matuszak, M., Kochmanski, P.: Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance. Precis. Eng. 44, 236–244 (2016)
Liu, L., Zhou, F., He, Y.: Automated status inspection of fastening bolts on freight trains using a machine vision approach. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit 230(7), 1629–1641 (2015)
Blasco, J., Munera, S., Aleixos, N., Cubero, S., Molto, E.: Machine vision-based measurement systems for fruit and vegetable quality control in Postharvest. Meas. Model. Autom. Adv. Food Process. 161, 71–91 (2017)
Park, J., Kwon, B., Park, J., et al.: Machine learning-based imaging system for surface defect inspection. Int. J. Precis. Eng. Manuf.-Green Tech. 3, 303–310 (2016)
Li, X., Shu, Y.: Research on glass surface quality inspection based on machine vision. J. Aust. J. Mech. Eng. 16, 98–104 (2018)
Zhang, X., Zhang, J., Ma, M., Chen, Z., Yue, S., He, T., Xu, X.: A high precision quality inspection system for steel bars based on machine vision. Sensors 18, 27–32 (2018)
Min, Y., Xiao, B., Dang, J., et al.: Real time detection system for rail surface defects based on machine vision. J. Image Video Process. 2018, 3 (2018). https://doi.org/10.1186/s13640-017-0241-y
Chen, Y.-J., Tsai, J.-C., Hsu, Y.-C.: A real-time surface inspection system for precision steel balls based on machine vision. Meas. Sci. Technol. 27(7), 25–31 (2016)
Zhou, Q., Chen, R., Huang, B., Liu, C., Yu, J., Yu, X.: An automatic surface defect inspection system for automobiles using machine vision methods. Sensors 19, 644 (2019)
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006
Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on Internet of Things collaborative control. IEEE Access 8, 32935–32946 (2020)
Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access 7, 105562–105571 (2019)
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Quan, L., Zicheng, Z., Jinjing, H., Chang, KC. (2021). Threshold Image Segmentation for Non-uniform Illumination Using Otsu Optimization Approach. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_93
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DOI: https://doi.org/10.1007/978-3-030-69717-4_93
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