Skip to main content
Log in

A high performance neural network and fuzzy logic based edge corner detector (NF-ECD)

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript


In recent years, Keypoints-based image detection algorithms have become essential to many image processing applications. They should be stable and invariant especially against image distortion and noise caused by different illumination conditions. Thus, the challenge is to design a faster and more robust detector in terms of accuracy and saliency of the detected keypoints. Toward this objective, the flexibility of artificial intelligence (AI) and its ability to learn and adapt has made it the primary choice to achieve this goal. In this paper, we propose a novel detector that combines the power of neural networks to detect robust feature points and fuzzy logic to select among them only the most significant. A neural network is implemented as a supervised machine learning technique. It is trained on a predefined database of straight edges (SEs) with different patterns representing a set of flow directions. The aim is to decompose a given contour into a set of connected straight edges (SEs) and estimate the flow direction for each. The transition points between nonlinear SEs are classified as edge corners (ECs). Finally, the set of these ECs is pruned by a fuzzy logic system to keep only the significant ones based on key corner parameters that can highly contribute in the matching process. Experimental results demonstrate clearly the robustness and saliency of our newly proposed NF-ECD in extracting keypoints. In addition, the NF-ECD achieves the best performance as compared to the state of the art keypoints detection algorithms. Using experiments conducted on the illumination set of the HPatches dataset, the repeatability score reaches 72.6%. On the other hand, the average computational time complexity obtained using the Object Recognition Dataset reaches 2.18 s which is the lowest among other similar detectors. In addition, NF-ECD shows an effective reduction in the matching runtime.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The authors declare that all data underlying the results are available as part of the article and no additional source data are required.


  1. Ahmed ST, Sharmila S (2020) Investigative protocol design of layer optimized image compression in telemedicine environment, Procedia Comput Sci 167. p. 2617–2622

  2. Balntas V, Lenc K, Vedaldi A, Mikolajczyk K (2017) Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5173–5182

  3. Basak J, Mahata D (2000) A connectionist model for corner detection in binary and gray images. IEEE Trans Neural Netw 11(5):1124–1132.

    Article  Google Scholar 

  4. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (surf), in Computer Vision and Image Understanding

  5. Csurka G, Dance CR, Humenberger M (2018) From handcrafted to deep local features, in arXiv preprint arXiv:1807.10254

  6. Dias PGT, Kassim AA, Srinivasan V (1995) A neural-network-based corner detection method. In IEEE Int. Conf. Neural Networks. pp. 2116–2120

  7. Dias PGT, Kassim AA, Srinivasan V (1995) A neural network based corner detection method, Proceedings of ICNN'95 - International Conference on Neural Networks. pp. 2116–2120 vol.4.

  8. Dusmanu M, Rocco I, Pajdla T, Pollefeys M, Sivic J, Torii A, Sattler T (2019) D2-net: A trainable cnn for joint detection and description of local features, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

  9. Foo JJ, Sinha R (2007) Pruning sift for scalable near-duplicate image matching, in Proceedings of the eighteenth conference on Australasian database - Volume 63. pp. 63–71

  10. Foo JL, Hardmeier H, Sattler T, Pollefeys M (2017) Comparative evaluation of hand-crafted and learned local features, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1482–1491

  11. Garibaldi J, John R (2003) Choosing membership functions of linguistic terms, in Proc 12th IEEE International Conference on Fuzzy Systems, vol. 1. pp. 578–583

  12. Harris C, Stephens M (1988) A combined corner and edge detector, in Proc. Alvey Vision Conf., Univ. Manchester

  13. Horak K, Klečka J, Boštík O, Davídek D (2017) Classification of SURF Image Features by Selected Machine Learning Algorithms, 40th International Conference on Telecommunications and Signal Processing (TSP).

  14. JCGM 100 (2008) Evaluation of measurement data – guide to the expression of uncertainty in measurement, Joint Committee for Guides in Metrology

  15. Kirsch R (1971) Computer determination of the constituent structure of biological images. Comput Biomed Res 4:315–328

    Article  Google Scholar 

  16. Laguna AB, Mikolajczyk K (2022) Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters Revisited, in IEEE Transactions on Pattern Analysis and Machine Intelligence.

  17. Lee K, Bien Z (1996) Grey-level corner detector using fuzzy logic, Pattern Recognition Lett, vol. 17. pp. 939–950

  18. Leutenegger S, Chli M, Siegwart RY (2011) BRISK: Binary Robust invariant scalable keypoints, International Conference on Computer Vision. pp. 2548–2555, 2011.

  19. Li B, Li H, Söderström U (2014) Scale-invariant corner keypoints, IEEE International Conference on Image Processing (ICIP). pp. 5741–5745

  20. Lindeberg T (2009) Scale Space, Encyclopedia of Computer Science and Engineering, Vol. IV, Benjamin Wah (ed.) (John Wiley and Sons).

  21. Lowe DG(2004) Distinctive image features from scale-invariant keypoints, in International booktitle of computer vision

  22. Lowe D (2004) Distinctive image features from scale-invariant keypoints, Int J Comput Vis 60, 91. p. 110

  23. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Stud 7(1):1–13.

    Article  MATH  Google Scholar 

  24. Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector, in European Conf. Computer Vision (Springer)

  25. Mikolajczyk K, Schmid C (2004), Scale & affine invariant interest point detectors, in ICCV.

  26. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10. IEEE. pp 1615–1630

  27. Nachar R, Inaty E, Bonnin P, Alayli Y (2014) A robust edge based corner detector (ebcd), IJIG, vol. 14, issue 04

  28. Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2.

  29. Olson D, Delen D (2008) Advanced Data Mining Techniques, Springer, 1st edition. pp.138, ISBN 3–540–76916-1

  30. Ono Y, Trulls E, Fua P, Yi KM (2018) Lf-net: learning local features from images, in Advances in Neural Information Processing Systems. pp. 6234–6244

  31. Pimenov V (2009) Fast image matching with visual attention and surf descriptors, in Proceedings of the 19th International Conference on Computer Graphics and Vision. pp. 49–56

  32. Powers D (2011) Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. J Mach Learn Technol 2(1):37–63

    MathSciNet  Google Scholar 

  33. Rosten E, Drummond T (2006) Machine learning for high speed corner detection. Proc Ninth European Conf Comput Vision 1:430–443

    Google Scholar 

  34. Rosten E, Porter R, Drummond T (2010) Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119.

    Article  Google Scholar 

  35. Rublee E, Rabaud V, Konolige K, Bradski K (2011) ORB: An efficient alternative to SIFT or SURF. International Conference on Computer Vision. pp. 2564–2571.

  36. Sergieh HM, Egyed-Zsigmond E, Döller M, Coquil D, Pinon J, Kosch H (2012) Improving SURF Image Matching Using Supervised Learning, Eighth International Conference on Signal Image Technology and Internet Based Systems. pp. 230–237.

  37. Shi J, Tomasi C (1994) Good features to track, in 9th IEEE Conf. Computer Vision and Pattern Recognition (Springer)

  38. Smith SM, Brady M (1997) SUSAN - A New Approach to Low Level Image Processing. Int J Comput Vis 23(1):45–78

    Article  Google Scholar 

  39. Tsai DM (1997) Boundary based corner detection using neural networks, Pattern Recogn, vol. 30. pp. 85–97

  40. Tsai D (1997) Boundary-based corner detection using neural networks. Pattern Recogn 30(1):85–97, ISSN 0031-3203.

    Article  MATH  Google Scholar 

  41. Tyszkiewicz MJ, Fua P, Trulls E (2020) Disk: Learning local features with policy gradient, in Advances in neural information processing systems

  42. van Leekwijck W, Kerre E (1999) Defuzzification: criteria and classification. Fuzzy Sets Syst 108(2):159–178

    Article  MathSciNet  MATH  Google Scholar 

  43. Zeng L, Dong Z, Xu Y (2019) Robust corner and tangent point detection for strokes with deep learning approach, ArXiv abs/1903.00899

  44. Zhang X, Yu FX, Karaman S, and Chang S-F(2017) Learning discriminative and transformation covariant local feature detectors, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6818–6826

  45. Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive, in Proc 28th Annual IEEE Conference of the Industrial Electronics Society, vol. 1. pp. 229–234

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rabih Nachar.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information


(PDF 305 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nachar, R., Inaty, E. & Bonnin, P.J. A high performance neural network and fuzzy logic based edge corner detector (NF-ECD). Multimed Tools Appl 82, 39459–39480 (2023).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: