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Threshold Image Segmentation for Non-uniform Illumination Using Otsu Optimization Approach

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

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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Authors and Affiliations

  1. School of Computer Science and Mathematics, Fujian University of Engineering, Fuzhou, China

    Liang Quan, Zhang Zicheng & Hu Jinjing

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, No3 Xueyuan Road, University Town, Minhou, Fuzhou, 350118, Fujian, China

    Liang Quan, Zhang Zicheng, Hu Jinjing & Kuo-Chi Chang

  3. School of Information Science and Engineering, Fujian University of Technology, No3 Xueyuan Road, University Town, Minhou, Fuzhou, 350118, Fujian, China

    Kuo-Chi Chang

Authors
  1. Liang Quan
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  2. Zhang Zicheng
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  3. Hu Jinjing
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  4. Kuo-Chi Chang
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Corresponding author

Correspondence to Zhang Zicheng .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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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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