Highly Repeatable Feature Point Detection in Images Using Laplacian Graph Centrality

Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Image registration is an indispensible task required in many image processing applications, which geometrically aligns multiple images of a scene, with differences caused due to time, viewpoint or by heterogeneous sensors. Feature-based registration algorithms are more robust to handle complex geometrical and intensity distortions when compared to area-based techniques. A set of appropriate geometrically invariant features forms the cornerstone for a feature-based registration framework. Feature point or interest point detectors extract salient structures such as points, lines, curves, regions, edges, or objects from the images. A novel interest point detector is presented in this paper. This algorithm computes interest points in a grayscale image by utilizing a graph centrality measure derived from a local image network. This approach exhibits superior repeatability in images where large photometric and geometric variations are present. The practical utility of this highly repeatable feature detector is evident from the simulation results.


Image registration Feature point Repeatability Graph centrality 


  1. 1.
    Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000CrossRefGoogle Scholar
  2. 2.
    Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the 7th European conference on computer vision-part I, ECCV’02. Springer, London, pp 128–142. ISBN 3-540-43745-2CrossRefGoogle Scholar
  3. 3.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision (ICCV’99), vol 2. IEEE Computer Society, USA, p 1150. ISBN 0-7695-0164-8Google Scholar
  4. 4.
    Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110. ISSN 0920-5691MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86. ISSN 0920-5691Google Scholar
  6. 6.
    Miao Z, Jiang X (2013) Interest point detection using rank order log filter. Pattern Recognit 46(11):2890–2901CrossRefGoogle Scholar
  7. 7.
    Criado R, Romance M, Sfanchez A (2012) Interest point detection in images using complex network analysis. J Comput Appl Math 236(12):2975–2980. ISSN 0377-0427MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ding G, Dai Q, Xu W, Yang F (2005) Affine-invariant image retrieval based on wavelet interest points. In: 2005 IEEE 7th workshop on multimedia signal processing, pp 1–4Google Scholar
  9. 9.
    Saydam SR, Rube IAE, Shoukry AA (2008) Contourlet based interest points detector. In: 2008 20th IEEE international conference on tools with artificial intelligence, vol 2, pp 509–513Google Scholar
  10. 10.
    Gevrekci M, Gunturk BK (2008) Reliable interest point detection under large illumination variations. In: 2008 15th IEEE international conference on image processing, pp 869–872Google Scholar
  11. 11.
    Maver J (2009) Self-similarity and points of interest. IEEE Trans Pattern Anal Mach Intell 32:1211–1226. ISSN 0162-8828CrossRefGoogle Scholar
  12. 12.
    Lee WT, Chen HT (2009) Histogram-based interest point detectors. In: 2009 IEEE conference on computer vision and pattern recognition, pp 1590–1596Google Scholar
  13. 13.
    Martins P, de Carvalho P (2009) On interest point detection under a landmark-based medical image registration context. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE, pp 2529–2532Google Scholar
  14. 14.
    Xie H, Gao K, Zhang Y, Li J, Liu Y (2010) GPU-based fast scale invariant interest point detector. In: 2010 IEEE international conference on acoustics, speech and signal processing, pp 2494–2497Google Scholar
  15. 15.
    Li B, Xiao R, Li Z, Cai R, Lu BL, Zhang L (2011) Rank-sift: learning to rank repeatable local interest points. CVPR 2011:1737–1744Google Scholar
  16. 16.
    Zukal M, Cika P (2012) Corner detectors: evaluation of information content. In: 2012 35th international conference on telecommunications and signal processing, pp 763–767Google Scholar
  17. 17.
    Wen H, Sheng XY (2011) An improved SIFT operator-based image registration using cross-correlation information. In: 2011 4th international congress on image and signal processing (CISP), vol 2. IEEE, pp 869–873Google Scholar
  18. 18.
    Yanhai W, Cheng Z, Jing W, Nan W (2015) Image registration method based on surf and freak. In: 2015 IEEE international conference on signal processing, communications and computing (ICSPCC), pp 1–4Google Scholar
  19. 19.
    Ma W, Wen Z, Wu Y, Jiao L, Gong M, Zheng Y, Liu L (2017) Remote sensing image registration with modified sift and enhanced feature matching. IEEE Geosci Remote Sens Lett 14(1):3–7. ISSN 1545-598XCrossRefGoogle Scholar
  20. 20.
    Nasir H, Stankovic V, Marshall S (2010) Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. In: 2010 18th European signal processing conference, pp 299–303Google Scholar
  21. 21.
    Qi X, Fuller E, Wu Q, Wu Y, Zhang CQ (2012) Laplacian centrality: a new centrality measure for weighted networks. Inf Sci 194:240–253MathSciNetCrossRefGoogle Scholar
  22. 22.
    Qi X, Duval RD, Christensen K, Fuller E, Spahiu A, Wu Q, Wu Y, Tang W, Zhang C et al (2013) Terrorist networks, network energy and node removal: a new measure of centrality based on Laplacian energy. Soc Netw 2(01):19CrossRefGoogle Scholar
  23. 23.
    P. N. Pournami and V. K. Govindan, "Interest point detection based on Laplacian energy of local image network," 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2017, pp. 58–62Google Scholar
  24. 24.
    Schmid C, Mohr R, Bauckhage C (2000) Evaluation of interest point detectors. Int J Comput Vis 37(2):151–172CrossRefGoogle Scholar
  25. 25.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Insitute of Technology CalicutCalicutIndia

Personalised recommendations