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CCDA: a concise corner detection algorithm

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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. 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. Alvarez, L., Morales, F.: Affine morphological multiscale analysis of corners and multiple junctions. Int. J. Comput. Vis. 25(2), 95–107 (1997)

    Article  Google Scholar 

  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)

  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)

  7. Harris, C., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 147–151 (1988)

  8. Rosten, E., Drummond, T.: FAST machine learning for High-speed corner detection. In: 2006 European Conference on Computer Vision, pp. 1–14 (2006)

  9. Moravec, H.P.: Obstacle avoidance and navigation in the real world by a seeing Robot Rover. Technical Report, DTIC Document (1980)

  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. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

  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)

    Book  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  17. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: 11th European Conference on Computer Vision (ECCV 2010)

  18. Calonder, Michael, et al.: BRIEF: computing a local binary descriptor very fast. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)

    Article  Google Scholar 

  19. Rosin, P.L.: Measuring corner properties. Comput. Vis. Image Underst. 73(2), 291–307 (1999)

    Article  Google Scholar 

  20. Davis, L.S.: A survey of edge detection techniques. Comput. Graph. Image Process. 4(3), 248–260 (1975)

    Article  MathSciNet  Google Scholar 

  21. Dim, J.R., Takamura, T.: Alternative approach for satellite cloud classification: edge gradient application. Adv. Meteorol. 2013, 1–8 (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

  24. Krig, S.: Computer Vision Metrics—Survey, Taxonomy, and Analysis, pp. 370–378. Apress, New York (2014)

    Book  Google Scholar 

  25. Rosten, E., Drummond, T.: Machine Learning for high-speed corner detection. In: 9th European Conference on Computer Vision, Graz, pp. 430–443 (2006)

  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)

    Article  Google Scholar 

  27. Balntas, V., Lenc, K., Vedaldi, A.: HPatches: a benchmark and evaluation of handcrafted and learned local descriptors. In: Computer Vision & Pattern Recognition (2017)

Download references

Acknowledgements

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.

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Authors and Affiliations

  1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhiyong Peng & Jun Wu

  2. Guangxi Experiment Center of Information Science, Guilin, 541004, China

    Zhiyong Peng

  3. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin, 541004, China

    Zhiyong Peng

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, 74074, USA

    Guoliang Fan

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  1. Zhiyong Peng
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  2. Jun Wu
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  3. Guoliang Fan
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Correspondence to Jun Wu.

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Peng, Z., Wu, J. & Fan, G. CCDA: a concise corner detection algorithm. Machine Vision and Applications 30, 1029–1040 (2019). https://doi.org/10.1007/s00138-019-01035-7

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