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A Fast Line and Ellipse Detection on High Resolution Images

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Image and Graphics Technologies and Applications (IGTA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1611))

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Abstract

Line and ellipse are important image features in pattern recognition and computer vision. Many methods have been developed to extract line or ellipse in images separately but few try to detect them simultaneously. In this paper, a novel fast line and ellipse detection (FLED) method is proposed to detect line and ellipse simultaneously, even in high resolution images. At first, a detection framework (Pre-SGV) for high detection speed is proposed, which explicitly decomposes the detection into precalculate, segment, grouping, validation phases. Secondly, a simple but efficient algorithm is designed to segment the edges into line or arc candidates. Thirdly, the grouping constraints and fitting methods are further improved. Finally, validation are conducted to exclude erroneous detection. Experiments on synthetic images and real image dataset show that the proposed method, FLED, can robustly detect lines and ellipses fast and efficiently, especially for high resolution image (e.g. remote sensing image, the scanning image).

This work is supported by the Beijing Natural Science Foundation under Grant 7202103.

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Author information

Authors and Affiliations

  1. The Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Limin Liu

  2. Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China

    Dingzhe Li, Zhaoxi Li & Cai Meng

  3. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China

    Cai Meng

Authors
  1. Limin Liu
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  2. Dingzhe Li
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  3. Zhaoxi Li
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  4. Cai Meng
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Corresponding author

Correspondence to Limin Liu .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  3. Peking University, Beijing, China

    Yuxin Peng

  4. Beijing Institute of Technology, Beijing, China

    Yue Liu

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

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Liu, L., Li, D., Li, Z., Meng, C. (2022). A Fast Line and Ellipse Detection on High Resolution Images. In: Wang, Y., Ma, H., Peng, Y., Liu, Y., He, R. (eds) Image and Graphics Technologies and Applications. IGTA 2022. Communications in Computer and Information Science, vol 1611. Springer, Singapore. https://doi.org/10.1007/978-981-19-5096-4_4

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  • DOI: https://doi.org/10.1007/978-981-19-5096-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5095-7

  • Online ISBN: 978-981-19-5096-4

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