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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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References

  1. Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, AVC 1988, Manchester, UK, September, 1988, ed. by C.J. Taylor (Alvey Vision Club, 1988), pp. 1–6

  2. Noble, J.A.: Finding corners. Image Vision Comput. 6(2), 121 (1988)

    Article  Google Scholar 

  3. Rothfeder, J.L., Feng, S., Rath, T.M.: Using corner feature correspondences to rank word images by similarity. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2003, Madison, Wisconsin, USA, 16-22 June, 2003, (IEEE Computer Society, 2003), p. 30

  4. Biedl, T.C., Velázquez, L.E.R.: Orthogonal cartograms with at most 12 corners per face. Comput. Geom. 47(2), 282 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Xu, C., Zheng, Y., Wang, Z.: Semantic feature extraction for accurate eye corner detection. In: 19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA. (IEEE Computer Society, 2008), pp. 1–4

  6. Guillon, S., Donias, M.: Directional second order derivatives: application to edge and corner detection. In: 9th European Signal Processing Conference, EUSIPCO 1998, Island of Rhodes, Greece, 8-11 September, 1998, (IEEE, 1998), pp. 1–4

  7. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63 (2004)

    Article  Google Scholar 

  8. Gueguen, L., Pesaresi, M.: Multi scale Harris corner detector based on differential morphological decomposition. Pattern Recogni. Lett. 32(14), 1714 (2011)

    Article  Google Scholar 

  9. Ardizzone, E., Bruno, A., Mazzola, G.: Scale detection via keypoint density maps in regular or near-regular textures. Pattern Recognit. Lett. 34(16), 2071 (2013)

    Article  Google Scholar 

  10. Zhang, W., Sun, C.: Corner detection using second-order generalized Gaussian directional derivative representations. IEEE Trans. Pattern Anal. Mach. Intell. (2019). https://doi.org/10.1109/TPAMI.2019.2949302

  11. Pan, X., Zhu, J., Yu, H., Chen, L., Liu, Y., Li, L.: Robust corner detection with fractional calculus for magnetic resonance imaging. Biomed. Signal Process. Control. 63, 102112 (2021)

    Article  Google Scholar 

  12. Zhang, W., Sun, C.: Corner detection using second-order generalized Gaussian directional derivative representations. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1213 (2021)

    Article  Google Scholar 

  13. Smith, S.M., Brady, J.M.: SUSAN - a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45 (1997)

    Article  Google Scholar 

  14. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105 (2010). https://doi.org/10.1109/TPAMI.2008.275

    Article  Google Scholar 

  15. Mokhtarian, F., Suomela, R.: Robust image corner detection through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1376 (1998)

    Article  Google Scholar 

  16. He, X., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, August 23-26, 2004, (IEEE Computer Society, 2004), pp. 791–794

  17. Awrangjeb, M., Lu, G.: An improved curvature scale-space corner detector and a robust corner matching approach for transformed image identification. IEEE Trans. Image Process. 17(12), 2425 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Awrangjeb, M., Lu, G.: Robust image corner detection based on the Chord-to-point distance accumulation technique. IEEE Trans. Multimed. 10(6), 1059 (2008)

    Article  Google Scholar 

  19. Shui, P., Zhang, W.: Corner detection and classification using anisotropic directional derivative representations. IEEE Trans. Image Process. 22(8), 3204 (2013)

    Article  Google Scholar 

  20. Zhang, W., Sun, C., Breckon, T.P., Alshammari, N.: Discrete curvature representations for noise robust image corner detection. IEEE Trans. Image Process. 28(9), 4444 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  21. Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679 (1986)

    Article  Google Scholar 

  22. Laligant, O., Truchetet, F.: A nonlinear derivative scheme applied to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 242 (2010)

    Article  Google Scholar 

  23. Kimia, B.B., Li, X., Guo, Y., Tamrakar, A.: Differential geometry in edge detection: accurate estimation of position, orientation and curvature. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1573 (2019)

    Article  Google Scholar 

  24. Zhang, X., Qu, Y., Yang, D., Wang, H., Kymer, J.D.: Laplacian scale-space behavior of planar curve corners. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2207 (2015)

    Article  Google Scholar 

  25. Chen, S., Meng, H., Zhang, C., Liu, C.: A KD curvature based corner detector. Neurocomputing 173, 434 (2016)

    Article  Google Scholar 

  26. Deriche, R., Giraudon, G.: A computational approach for corner and vertex detection. Int. J. Comput. Vis. 10(2), 101 (1993)

    Article  Google Scholar 

  27. Peng, Z., Wu, J., Fan, G.: CCDA: a concise corner detection algorithm. Mach. Vis. Appl. 30(6), 1029 (2019)

    Article  Google Scholar 

  28. Debelee, T.G., Schwenker, F., Rahimeto, S., Yohannes, D.: Evaluation of modified adaptive k-means segmentation algorithm. Comput. Vis. Media 5(4), 347 (2019)

    Article  Google Scholar 

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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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