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Research on Image Segmentation of Complex Environment Based on Variational Level Set

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Abstract

An improved image segmentation model was established to achieve accurate detection of target contours under high noise, low resolution, and uneven illumination environments. The new model is based on the variational level set algorithm, which improves the C-V (Chan and Vese) model, fuses the contour and area models to segment the image information, and solves the problem of optimal solution of the energy model by finding the steady-state solution of the partial differential equation. It can improve the calculation accuracy, topological structure adaptability, anti-noise ability, and reduce the light sensitivity effectively. Experiment shows that the new model has good robustness, high real-time performance, and it can effectively improve detection accuracy.

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Acknowledgement

This work is partly supported by the Science and Technology Project of Jiangsu Provincial Department of Housing and Construction (2019ZD039), Major Projects of Natural Science Research in Universities of Jiangsu Province (19KJA470002).

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Authors and Affiliations

  1. Xuzhou University of Technology, Xuzhou, 221000, Jiangsu, China

    Hang Li, Dan Li, Kailiang Zhang & Chuangeng Tian

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  1. Hang Li
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  2. Dan Li
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  3. Kailiang Zhang
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  4. Chuangeng Tian
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Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, H., Li, D., Zhang, K., Tian, C. (2021). Research on Image Segmentation of Complex Environment Based on Variational Level Set. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_55

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  • DOI: https://doi.org/10.1007/978-3-030-72795-6_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72794-9

  • Online ISBN: 978-3-030-72795-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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