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IPCS: An improved corner detector with intensity, pattern, curvature, and scale

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Abstract

The corner detection plays an important role in the area of image processing and computer vision. The current corner detection methods often utilize few cues or single model to improve the detection correctness and repeatability. A composite model of both intensity, pattern, curvature, and scale is proposed as a possible solution to these problems. Firstly, a corner measure function that reflects both the intensity, pattern, and curvature difference is formulated based on the 8-neighbor pixel blocks. Secondly, some scale-based global scale importance factors are formulated based on the contour distribution and corner distribution. Thirdly, based on the corner measure and the importance factors, a high-performance corner detector (IPCS) is derived. The experiments based on both the ground truth and the standard image set are conducted to evaluate the correctness and repeatability of the proposed detector. The experiment results come up with that the proposed detector has remarkable performance advantages among the comprising state-of-the-art detectors.

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Acknowledgements

The authors would like to thank the support of the Program of Huizhou City Science and Technology Plan No. 2017C0409025.

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Huizhou University, 516007, Guangdong, China

    Changlin Wan, Jianzhong Cao, Xiaohui Wei, Jingqiu Huang, Zhiming Chen & Deming Xu

  2. Jiangxi Institute of Computing Technology, 330003, Jiangxi, China

    Feng Qiu

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  1. Changlin Wan
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  2. Jianzhong Cao
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  3. Xiaohui Wei
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  4. Jingqiu Huang
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  5. Zhiming Chen
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  6. Deming Xu
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  7. Feng Qiu
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Correspondence to Changlin Wan.

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Wan, C., Cao, J., Wei, X. et al. IPCS: An improved corner detector with intensity, pattern, curvature, and scale. Vis Comput 39, 2499–2513 (2023). https://doi.org/10.1007/s00371-022-02474-6

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