Skip to main content
SpringerLink
Log in

A corner detection method based on adaptive multi-directional anisotropic diffusion

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

Abstract

As the most significant feature, corner is widely used in many application areas of computer vision, such as object tracking, image restoration, and 3D reconstruction. Various noises in the image bring about non-negligible negative impact on the accuracy of feature location for a corner detection algorithm. To avoid the influence of noise, the Gaussian filter was utilized by existing algorithms, which lead to loss of detailed information of image edges or even loss of corners. Considering the geometric structure of the corners, a new anisotropic diffusion method taking into consideration the image local multi-directional information was designed at first to achieve significant denoising effect and preserve the edge information and detailed information of the image. Subsequently, a multi-directional structure tensor product is applied to construct feasible corner measure function for detecting corners with high robustness. Finally, metrics about location accuracy, average repeatability, and image matching performance were applied to evaluated the performance of proposed corner detection method. Compare with twelve state-of-the-art methods, the experiments show that the proposed method is optimal in overall performance and the average score is 0.8504. Comparing with other methods, the proposed method has 1%-24% improvement in average performance with image affine transformation. The corner location error is 1.2216 on ‘Lab’, 1.2617 on ‘Block’ and 1.7002 on ‘Pentagon’, which are better than other detectors. In experiment with light and viewpoint changes, our proposed method outperforms other methods by 2.7% to 35.76% on average matching score.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. http://peterkovesi.com/matlabfns/

  2. Alcantarilla PF, Bartoli A, Davison AJ (2012) KAZE features. In: European conference on computer vision, pp 214–227

  3. Ando S (2000) Image field categorization and edge/corner detection from gradient covariance. IEEE Trans Pattern Anal Mach Intell 22(2):179–190

    Article  Google Scholar 

  4. Awrangjeb M, Lu G (2008) Robust image corner detection based on the chord-to-point distance accumulation technique. IEEE Transactions on Multimedia 10(6):1059–1072

    Article  Google Scholar 

  5. 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

  6. Barroso-Laguna A, Riba E, Ponsa D, Mikolajczyk K (2019) Key. net: Keypoint detection by handcrafted and learned CNN filters. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 5836–5844

  7. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  8. Bennett S, Lasenby J (2014) Chess–quick and robust detection of chess-board features. Comput Vis Image Underst 118:197–210

    Article  Google Scholar 

  9. Bigu J, et al. (1990) A structure feature for some image processing applications based on spiral functions. Computer Vision, Graphics, and Image Processing 51 (2):166–194

    Article  Google Scholar 

  10. Brox T, Weickert J, Burgeth B, Mrázek P (2006) Nonlinear structure tensors. Image Vis Comput 24(1):41–55

    Article  Google Scholar 

  11. Chandrakar R, Raja R, Miri R (2021) Animal detection based on deep convolutional neural networks with genetic segmentation. Multimedia Tools and Applications, pp 1–14

  12. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 248–255

  13. Deriche R, Giraudon G (1993) A computational approach for corner and vertex detection. Int J Comput Vis 10(2):101–124

    Article  Google Scholar 

  14. Dusmanu M, Rocco I, Pajdla T, Pollefeys M, Sivic J, Torii A, Sattler T (2019) D2-Net: A trainable CNN for joint description and detection of local features. In: Proceedings of the conference on computer vision and pattern recognition, pp 8092–8101

  15. Duval-Poo MA, Odone F, De Vito E (2015) Edges and corners with shearlets. IEEE Trans Image Process 24(11):3768–3780

    Article  MathSciNet  MATH  Google Scholar 

  16. Förstner W, Dickscheid T, Schindler F (2009) Detecting interpretable and accurate scale-invariant keypoints. In: IEEE 12th international conference on computer vision, pp 2256–2263

  17. Harris C G, Stephens (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15, pp 10–5244

  18. Hasegawa T, Yamauchi Y, Ambai M, Yoshida Y, Fujiyoshi H (2014) Keypoint detection by cascaded fast. In: IEEE international conference on image processing, pp 5676–5680

  19. He X, Yung NHC (2008) Corner detector based on global and local curvature properties. Opt Eng 47(5):057008

    Article  Google Scholar 

  20. Jing J, Gao T, Zhang W, Gao Y, Sun C (2021) Image feature information extraction for interest point detection: A comprehensive review. arXiv preprint arXiv:2106.07929

  21. Kenney C S, Zuliani M, Manjunath BS (2005) An axiomatic approach to corner detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 191–197

  22. Kohlmann K (1996) Corner detection in natural images based on the 2-D Hilbert transform. Signal Process 48(3):225–234

    Article  MATH  Google Scholar 

  23. Lenc K, Gulshan AVV (2011). http://www.vlfeat.org/benchmarks/xsxs

  24. Lowe D G (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Mainali P, Lafruit G, Yang Q, Geelen B, Van Gool L, Lauwereins R (2013) SIFER: Scale-invariant feature detector with error resilience. Int J Comput Vis 104(2):172–197

    Article  MATH  Google Scholar 

  26. Mainali P, Yang Q, Lafruit G, Van Gool L, Lauwereins R (2011) Robust low complexity corner detector. IEEE Transactions on Circuits and Systems for Video Technology 21(4):435–445

    Article  Google Scholar 

  27. Mair E, Hager G D, Burschka D, Suppa M, Hirzinger G (2010) Adaptive and generic corner detection based on the accelerated segment test. In: European conference on computer vision, pp 183–196

  28. Mehrotra R, Nichani S, Ranganathan N (1990) Corner detection. Pattern Recogn 23(11):1223–1233

    Article  Google Scholar 

  29. Miao Z, Jiang X (2013) Interest point detection using rank order log filter. Pattern Recogn 46(11):2890–2901

    Article  Google Scholar 

  30. Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vis 60(1):63–86

    Article  Google Scholar 

  31. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1):43–72

    Article  Google Scholar 

  32. Mishchuk A, Mishkin D, Radenović F, Matas J (2017) Working hard to know your neighbor’s margins: Local descriptor learning loss. In: Advances in neural information processing systems, pp 4827–4838

  33. Moravec HP (1979) Visual mapping by a robot rover. In: Proceedings of the 6th international joint conference on artificial intelligence-volume 1, pp 598–600

  34. Nguyen TP, Debled-Rennesson I (2011) A discrete geometry approach for dominant point detection. Pattern Recogn 44(1):32–44

    Article  MATH  Google Scholar 

  35. Olson CF (2000) Adaptive-scale filtering and feature detection using range data. IEEE Trans Pattern Anal Mach Intell 22(9):983–991

    Article  Google Scholar 

  36. Ono Y, Trulls E, Fua P, Yi KM (2018) LF-Net: Learning local features from images. In: Proceedings of the 32nd international conference on neural information processing systems, pp 6237–6247

  37. Parida L, Geiger D, Hummel R (1998) Junctions: Detection, classification, and reconstruction. IEEE Trans Pattern Anal Mach Intell 20(7):687–698

    Article  Google Scholar 

  38. Pedrosa GV, Barcelos CA (2010) Anisotropic diffusion for effective shape corner point detection. Pattern Recogn Lett 31(12):1658–1664

    Article  Google Scholar 

  39. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  40. Raja R, Kumar S, Mahmood MR (2020) Color object detection based image retrieval using ROI segmentation with multi-feature method. Wirel Pers Commun 112(1):169–192

    Article  Google Scholar 

  41. Raja R, Sinha TS, Patra RK, Tiwari S (2018) Physiological trait-based biometrical authentication of human-face using LGXP and ANN techniques. Int J Inf Comput Secur 10(2-3):303–320

    Google Scholar 

  42. Revaud J, De Souza C, Humenberger M, Weinzaepfel P (2019) R2d2: Reliable and repeatable detector and descriptor. Adv Neural Inform Process Syst 32:12405–12415

    Google Scholar 

  43. Rosten E, Porter R, Drummond T (2008) Faster and better: A machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32 (1):105–119

    Article  Google Scholar 

  44. Shen X, Wang C, Li X, Yu Z, Li J, Wen C, Cheng M, He Z (2019) RF-Net: An end-to-end image matching network based on receptive field. In: Proceedings of the conference on computer vision and pattern recognition, pp 8132–8140

  45. Shui P-L, Zhang W-C (2012) Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels. Pattern Recogn 45(2):806–820

    Article  MATH  Google Scholar 

  46. Shui P-L, Zhang W-C (2013) Corner detection and classification using anisotropic directional derivative representations. IEEE Trans Image Process 22(8):3204–3218

    Article  Google Scholar 

  47. Smith SM, Brady JM (1997) SUSAN–A new approach to low level image processing. Int J Comput Vis 23(1):45–78

    Article  Google Scholar 

  48. Teng SW, Sadat RMN, Lu G (2015) Effective and efficient contour-based corner detectors. Pattern Recogn 48(7):2185–2197

    Article  Google Scholar 

  49. Tiwari L, Raja R, Awasthi V, Miri R, Sinha GR, Alkinani MH, Polat K (2021) Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms. Measurement 172:108882

    Article  Google Scholar 

  50. Van de Weijer J, Gevers T, Geusebroek J-M (2005) Edge and corner detection by photometric quasi-invariants. IEEE Trans Pattern Anal Mach Intell 27(4):625–630

    Article  Google Scholar 

  51. Wang M, Zhang W, Sun C, Sowmya A (2020) Corner detection based on shearlet transform and multi-directional structure tensor. Pattern Recogn 103:107299

    Article  Google Scholar 

  52. Xia G-S, Delon J, Gousseau Y (2014) Accurate junction detection and characterization in natural images. Int J Comput Vis 106(1):31–56

    Article  MathSciNet  MATH  Google Scholar 

  53. Xue N, Xia G-S, Bai X, Zhang L, Shen W (2017) Anisotropic-scale junction detection and matching for indoor images. IEEE Trans Image Process 27 (1):78–91

    Article  MathSciNet  MATH  Google Scholar 

  54. Yan P, Tan Y, Tai Y, Wu D, Luo H, Hao X (2021) Unsupervised learning framework for interest point detection and description via properties optimization. Pattern Recogn 112:107808

    Article  Google Scholar 

  55. Yi KM, Trulls E, Lepetit V, Fua P (2016) LIFT: Learned invariant feature transform. In: European conference on computer vision, Springer, pp 467–483

  56. Zhang W, Sun C (2021) Corner detection using second-order generalized Gaussian directional derivative representations. IEEE Trans Pattern Anal Mach Intell 43(4):1213–1224

    Article  Google Scholar 

  57. Zhang W-C, Shui P-L (2015) Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives. Pattern Recogn 48(9):2785–2797

    Article  Google Scholar 

  58. Zhang W-C, Wang F-P, Zhu L, Zhou Z-F (2014) Corner detection using gabor filters. IET Image Process 8(11):639–646

    Article  Google Scholar 

  59. Zhang W, Sun C (2020) Corner detection using multi-directional structure tensor with multiple scales. Int J Comput Vis 128(2):438–459

    Article  MathSciNet  MATH  Google Scholar 

  60. Zhang W, Sun C, Breckon T, Alshammari N (2019) Discrete curvature representations for noise robust image corner detection. IEEE Trans Image Process 28(9):4444–4459

    Article  MathSciNet  MATH  Google Scholar 

  61. Zhang W, Zhao Y, Breckon TP, Chen L (2017) Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recogn 63:193–205

    Article  Google Scholar 

  62. Zhang X, Wang H, Smith AW, Ling X, Lovell BC, Yang D (2010) Corner detection based on gradient correlation matrices of planar curves. Pattern Recogn 43(4):1207–1223

    Article  MATH  Google Scholar 

  63. Zhang X, Yu FX, Karaman S, 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

  64. Zhong B, Liao W (2007) Direct curvature scale space: Theory and corner detection. IEEE Trans Pattern Anal Mach Intell 29(3):508–512

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by Innovation Capability Support Program of Shaanxi (No.2021TD-29), in part by the Youth Innovation Team of Shaanxi Universities, in part by Key Research and Development Program of Shaanxi (No.2022GY-066), and in part by the National Natural Science Foundation of China (No.62176204).

Author information

Authors and Affiliations

  1. School of Electronic Information, Xi’an Polytechnic University, 19 Jinhua South Road, Xi’an, 710048, Shaanxi, China

    Junmin Bao, Junfeng Jing, Weichuan Zhang, Chao Liu & Tian Gao

Authors
  1. Junmin Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junfeng Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weichuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tian Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junfeng Jing.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bao, J., Jing, J., Zhang, W. et al. A corner detection method based on adaptive multi-directional anisotropic diffusion. Multimed Tools Appl 81, 28729–28754 (2022). https://doi.org/10.1007/s11042-022-12666-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12666-w

Keywords

Search

Navigation