CCDA: a concise corner detection algorithm

  • Zhiyong Peng
  • Jun WuEmail author
  • Guoliang Fan
Original Paper


In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second derivative non-maximum suppression is used to get the location of the corner and can get the exact corner point. As a result, CCDA is compare-tested with classical corner detection algorithms by using the same images which include synthetic corner patterns and real images. The result shows that CCDA has a similar speed to the FAST algorithm and better accuracy and robustness than the HARRIS algorithm.


Corner detection Cascade classifier Gradient direction Non-maximum suppression 



This work was supported in part by National Natural Science Foundation of China (41761087) and Guangxi Natural Science Foundation (2017GXNSFAA198162), by Foundation of Guangxi Experiment Center of Information Science (YB1414), by Innovation Project of Guangxi Graduate Education (YCBZ2017051), by Guangxi College’s emphasis laboratory foster base for optoelectronics information (handling) Project (GD18108), and by the study abroad program for graduate student of Guilin University of Electronic Technology.


  1. 1.
    Yan, C., Xie, H., Chen, J., Zha, Z., Hao, X., Zhang, Y., Dai, Q.: A fast Uyghur text detector for complex background images. IEEE Trans. Multimed. 20(12), 3389–3398 (2018)CrossRefGoogle Scholar
  2. 2.
    Zhang, S., Liu, W.: Single image 3D reconstruction based on control point grid. Multimed. Tools Appl. 77(3), 1–19 (2018)Google Scholar
  3. 3.
    Yan, C., Li, L., Zhang, C., Liu, B., Zhang, Yongdong, Dai, Q.: Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans. Multimed. 6(3), 1–10 (2019)Google Scholar
  4. 4.
    Alvarez, L., Morales, F.: Affine morphological multiscale analysis of corners and multiple junctions. Int. J. Comput. Vis. 25(2), 95–107 (1997)CrossRefGoogle Scholar
  5. 5.
    Mokhtarian, F., Suomela, R.: Curvature scale space for robust image corner detection. In: 1998 IEEE International Conference on Pattern Recognition, pp. 1819–1823 (1998)Google Scholar
  6. 6.
    Alvarez, L.: Corner detection using the affine morphological scale space. In: 2017 IEEE International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2017), pp. 29–40 (2017)Google Scholar
  7. 7.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  8. 8.
    Rosten, E., Drummond, T.: FAST machine learning for High-speed corner detection. In: 2006 European Conference on Computer Vision, pp. 1–14 (2006)Google Scholar
  9. 9.
    Moravec, H.P.: Obstacle avoidance and navigation in the real world by a seeing Robot Rover. Technical Report, DTIC Document (1980)Google Scholar
  10. 10.
    Schmid, Cordelia, Mohr, Roger: Local gray value invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. Inst. Electr. Electron. Eng. 19(5), 530–534 (1997)Google Scholar
  11. 11.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  12. 12.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, Computational Imaging and Vision Series, pp. 38–42. Springer, New York (2011)CrossRefGoogle Scholar
  13. 13.
    Smith, S.M., Brady, J.M.: SUSAN—a new approach to low level image processing. Technical Report TR95SMS1c (patended), Crown Copyright (1995), Defence Research Agency, UK (1995)Google Scholar
  14. 14.
    Smith, S.M., Michael Brady, J.: SUSAN—a new approach to low level image processing. Int. J. Comput. Vis. Arch. 23(1), 45–78 (1997)CrossRefGoogle Scholar
  15. 15.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV 2011), pp. 1–8 (2011)Google Scholar
  16. 16.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV 2011), pp. 1–8 (2011)Google Scholar
  17. 17.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: 11th European Conference on Computer Vision (ECCV 2010)Google Scholar
  18. 18.
    Calonder, Michael, et al.: BRIEF: computing a local binary descriptor very fast. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  19. 19.
    Rosin, P.L.: Measuring corner properties. Comput. Vis. Image Underst. 73(2), 291–307 (1999)CrossRefGoogle Scholar
  20. 20.
    Davis, L.S.: A survey of edge detection techniques. Comput. Graph. Image Process. 4(3), 248–260 (1975)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Dim, J.R., Takamura, T.: Alternative approach for satellite cloud classification: edge gradient application. Adv. Meteorol. 2013, 1–8 (2013)Google Scholar
  22. 22.
    Hast, A.: Simple filter design for first and second order derivatives by a double filtering approach. Pattern Recognit. Lett. 42(1), 65–71 (2014)CrossRefGoogle Scholar
  23. 23.
    Gunn, S.R.: Edge detection error in the discrete Laplacian of Gaussian. In: 1998 International Conference on Image Processing (ICIP 98), pp. 515–519 (1998)Google Scholar
  24. 24.
    Krig, S.: Computer Vision Metrics—Survey, Taxonomy, and Analysis, pp. 370–378. Apress, New York (2014)CrossRefGoogle Scholar
  25. 25.
    Rosten, E., Drummond, T.: Machine Learning for high-speed corner detection. In: 9th European Conference on Computer Vision, Graz, pp. 430–443 (2006)Google Scholar
  26. 26.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1–2), 43–72 (2005)CrossRefGoogle Scholar
  27. 27.
    Balntas, V., Lenc, K., Vedaldi, A.: HPatches: a benchmark and evaluation of handcrafted and learned local descriptors. In: Computer Vision & Pattern Recognition (2017)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Electronic Engineering and AutomationGuilin University of Electronic TechnologyGuilinChina
  2. 2.Guangxi Experiment Center of Information ScienceGuilinChina
  3. 3.Guangxi Key Laboratory of Automatic Detecting Technology and InstrumentsGuilinChina
  4. 4.School of Electrical and Computer EngineeringOklahoma State UniversityStillwaterUSA

Personalised recommendations