Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31139–31157 | Cite as

Fast point matching using corresponding circles

  • Abderazzak TaimeEmail author
  • Abderrahim Saaidi
  • Khalid Satori


Point matching via corresponding circles (ICC) is a technique for removing outliers (mismatches) from given putative point correspondences in image pairs. It can be used as a basis for a wide range of applications including structure-from-motion, 3D reconstruction, tracking, image retrieval, registration, and object recognition. In this paper, we propose a new method called Fast Identification of point correspondences by Corresponding Circles (FICC) that improves the quality of the rejection mismatches and reduces the cost of computing it. In particular, we propose a new strategy that aims to take better advantage of the corresponding circles and reduces the number of putative points correspondences tested by the corresponding circles in each iteration rather than all set of putative correspondences, as in the original ICC. This reduces the computing time and together with a more efficient tool for rejecting mismatches which leads to significant gains in efficiency. We provide comparative results illustrating the improvements obtained by FICC over ICC, and we compare with many state-of-the-art methods.


Matching Corresponding circles Outliers Putative correspondences 


  1. 1.
    Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. Comput Vision–ECCV 2006, 404–417Google Scholar
  2. 2.
    Cao D-S, Liang Y-Z, Xu Q-S et al (2010) A new strategy of outlier detection for QSAR/QSPR. J Comput Chem 31(3):592–602Google Scholar
  3. 3.
    Chen YH, Huang HC (2013) A wavelet-based image watermarking scheme for stereoscopic video frames. In intelligent information hiding and multimedia signal processing, 2013 ninth international conference on (pp. 25–28). IEEEGoogle Scholar
  4. 4.
    Chum O,Matas, J (2005) Matching with PROSAC-progressive sample consensus. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE computer society conference on. IEEE: 220–226Google Scholar
  5. 5.
    Chum O, Matas J, Kittler J (2003) Locally optimized RANSAC. In: Pattern Recognition Symposium of the German Association for Pattern Recognition. Springer, Berlin, pp 236–243Google Scholar
  6. 6.
    Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396CrossRefGoogle Scholar
  7. 7.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395MathSciNetCrossRefGoogle Scholar
  8. 8.
    Fua P, Leclerc YG (1995) Object-centered surface reconstruction: combining multi-image stereo and shading. Int J Comput Vis 16(1):35–56CrossRefGoogle Scholar
  9. 9.
    Hirschmuller H, Scharstein D (2007) Evaluation of cost functions for stereo matching. In: Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE. p. 1–8Google Scholar
  10. 10.
    Huber PJ, Ronchetti EM (1981) Robust statistics, ser. Wiley Series in Probability and Mathematical Statistics. Wiley-IEEE, New York, 52, 54Google Scholar
  11. 11.
    Jian MW, Dong JY, Ma J (2011) Image retrieval using wavelet-based salient regions. Imaging Sci J 59(4):219–231CrossRefGoogle Scholar
  12. 12.
    Jian M, Lam KM, Dong J (2014) Facial-feature detection and localization based on a hierarchical scheme. Inf Sci 262:1–14CrossRefGoogle Scholar
  13. 13.
    Jian M, Lam KM, Dong J, Shen L (2015) Visual-patch-attention-aware saliency detection. IEEE Trans Cybernet 45(8):1575–1586CrossRefGoogle Scholar
  14. 14.
    Ke Y, Sukthankar R (2004) PCA-SIFT: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference vol. 2, pp. 506–513Google Scholar
  15. 15.
    Knuth, D. E (2011) The art of computer programming, volume 4A: Combinatorial Algorithms, Part 1. Pearson Education IndiaGoogle Scholar
  16. 16.
    Kumano M, Ohya A, Yuta S (2000) Obstacle avoidance of autonomous mobile robot using stereo vision sensor. In: Intl. Symp. Robot. Automat. p. 497–502Google Scholar
  17. 17.
    Li X, Hu Z (2010) Rejecting mismatches by correspondence function. Int J Comput Vis 89(1):1–17CrossRefGoogle Scholar
  18. 18.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110MathSciNetCrossRefGoogle Scholar
  19. 19.
    Massart DL, Kaufman L, Rousseeuw PJ et al (1986) Least median of squares: a robust method for outlier and model error detection in regression and calibration. Anal Chim Acta 187:171–179CrossRefGoogle Scholar
  20. 20.
    Mikolajczyk K et al (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72CrossRefGoogle Scholar
  21. 21.
    Scharstein D, Pal C (2007) Learning conditional random fields for stereo. In: Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on IEEE p 1–8Google Scholar
  22. 22.
    Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42CrossRefGoogle Scholar
  23. 23.
    Scharstein D, Szeliski R (2003) High-accuracy stereo depth maps using structured light. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 I.E. computer society conference on. IEEE. p. I-IGoogle Scholar
  24. 24.
    Scharstein D, et al. (2014) High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition. Springer International Publishing. p. 31–42Google Scholar
  25. 25.
    Sim K, Hartley R (2006) Removing outliers using the L\infty norm. In: Computer Vision and Pattern Recognition, 2006 I.E. computer society conference on. IEEE p 485–494Google Scholar
  26. 26.
    Taime A, Riffi J, Saaidi A, Satori K (2017) Robust point matching via corresponding circles. Multimed Tools Appl: 1–20Google Scholar
  27. 27.
    Torr PHS, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1):138–156CrossRefGoogle Scholar
  28. 28.
    Yang Y et al (2015) Multi-class active learning by uncertainty sampling with diversity maximization. Int J Comput Vis 113(2):113–127MathSciNetCrossRefGoogle Scholar
  29. 29.
    Yang J, Li F, Sun Z, Jiang S (2016) A small target detection method based on human visual system and confidence measurement. J Inform Hiding Multimedia Signal Process 7(2):448–459Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Abderazzak Taime
    • 1
    Email author
  • Abderrahim Saaidi
    • 1
    • 2
  • Khalid Satori
    • 1
  1. 1.LIIAN, Department of Mathematics and computer science Faculty of SciencesDhar-Mahraz Sidi Mohamed Ben Abdellah UniversityAtlas-FezMorocco
  2. 2.LSI, Department of Mathematics, Physics and Computer Science Polydisciplinary Faculty of TazaDhar-Mahraz Sidi Mohamed Ben Abdellah UniversityTazaMorocco

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