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Corner Detection Based on a Dynamic Measure of Cornerity

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13631))

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Abstract

Existing contour-based corner detectors generally identify corners from a contour curve by measuring the cornerity of each point (i.e., the confidence to be a corner) with a fixed-radius region of support (RoS), and thus could yield inferior performance due to low adaptivity to local structures of the input curve. To overcome the difficulty, a novel cornerity measure based on a dynamic RoS is proposed in this paper, with which an efficient corner detector is developed. For a given point on the curve, the dynamic RoS is constructed with two straight-line arms stretching towards both sides along the curve, under a pre-determined error tolerance imposed on the average perpendicular distance from the curve to each arm within its stretching range. Then, our cornerity model is established based on the lengths of the two arms and the angle between them, which is then exploited to evaluate whether the current point is a corner or not via a cornerity thresholding. Extensive experimental results show that the proposed corner detector can deliver superior performance and exhibit higher robustness over the existing state-of-the-arts.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 21KJA520007, in part by the National Natural Science Foundation of China under Grant 61572341, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yang Zhang, Baojiang Zhong & Xun Sun

Authors
  1. Yang Zhang
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  2. Baojiang Zhong
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  3. Xun Sun
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Corresponding author

Correspondence to Baojiang Zhong .

Editor information

Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Zhang, Y., Zhong, B., Sun, X. (2022). Corner Detection Based on a Dynamic Measure of Cornerity. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_47

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  • DOI: https://doi.org/10.1007/978-3-031-20868-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20867-6

  • Online ISBN: 978-3-031-20868-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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