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Robust point matching via corresponding circles

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Abstract

The matching points extracted from images play a very important role in many applications and particularly in computer vision. The use of point sets as being characteristics that describe the entire images brought into play, it greatly contributes to the reduction of the execution time, unlike the use of all the information contained in these images. The major problem of the matching process is the possibility to generate a large number of false correspondences, or outliers, in addition to a limited number of true correspondences (inliers). The objective of this paper is to propose a robust algorithm to eliminate or reduce the false correspondences, or outliers, among the putative set extracted from stereoscopic images. The principle of our method is based on the notion of belonging to the corresponding circles and the concept of similarity of stereoscopic images. The results largely reflect the efficiency and performance of our algorithm in comparison to the other used methods in this framework like RANSAC algorithm.

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References

  1. 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):592–602

    Google Scholar 

  2. 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). IEEE

  3. Chen J, Ma J, Yang C, Tian J (2014) Mismatch removal via coherent spatial relations. J Electron Imaging 23(4):043012–043012

    Article  Google Scholar 

  4. Chevrel M, Courtois M, Weill G (1981) The SPOT satellite remote sensing mission. Photogramm Eng Remote Sens 47:1163–1171

    Google Scholar 

  5. 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. p. 220-226

  6. Chum O, Matas J, Kittler J (2003) Locally optimized RANSAC. In: Pattern Recognition Symposium of the German Association for Pattern Recognition. Springer, Berlin, pp 236–243

  7. Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396

    Article  Google Scholar 

  8. 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):381–395

    Article  MathSciNet  Google Scholar 

  9. Fua P, Leclerc YG (1995) Object-centered surface reconstruction: combining multi-image stereo and shading. Int J Comput Vis 16(1):35–56

    Article  Google Scholar 

  10. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge university press, Cambridge

    MATH  Google Scholar 

  11. 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. 1–8

  12. Huber PJ, Ronchetti EM (1981) Robust statistics, ser. Wiley Series in Probability and Mathematical Statistics. Wiley-IEEE, New York, 52, 54

  13. Kumano M, Ohya A, Yuta S (2000) Obstacle avoidance of autonomous mobile robot using stereo vision sensor. In: Intl. Symp. Robot. Automat. p. 497–502

  14. Li H (2007) A practical algorithm for L-infinity triangulation with outliers. In proceedings of IEEE conference on computer vision and pattern recognition. 1–8

  15. Li X, Hu Z (2010) Rejecting mismatches by correspondence function. Int J Comput Vis 89(1):1–17

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Marr D, Poggio T, Hildreth EC et al. (1991) A computational theory of human stereo vision. In : From the Retina to the Neocortex. Birkhäuser Boston. p. 263–295

  18. 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 187:171–179

    Article  Google Scholar 

  19. Mikolajczyk K et al (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72

    Article  Google Scholar 

  20. Nistér D (2005) Preemptive RANSAC for live structure and motion estimation. Mach Vis Appl 16(5):321–329

    Article  Google Scholar 

  21. Rahmatullah Imon AHM (2005) Identifying multiple influential observations in linear regression. J Appl Stat 32(9):929–946

    Article  MathSciNet  MATH  Google Scholar 

  22. 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 1–8

  23. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42

    Article  MATH  Google Scholar 

  24. 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-I

  25. Scharstein D, et al. (2014) High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition. Springer International Publishing. p. 31–42

  26. 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 485-494

  27. Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage Learning

  28. Torr PHS (1997) Et Zisserman, Andrew. Robust parameterization and computation of the trifocal tensor. Image Vis Comput 15(8):591–605

    Article  Google Scholar 

  29. Torr PHS, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1):138–156

    Article  Google Scholar 

  30. Yang Y et al (2015) Multi-class active learning by uncertainty sampling with diversity maximization. Int J Comput Vis 113(2):113–127

    Article  MathSciNet  Google Scholar 

  31. 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):448–459

    Google Scholar 

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Authors and Affiliations

  1. LIIAN, Department of Mathematics and computer science Faculty of Sciences, Dhar-Mahraz Sidi Mohamed Ben Abdellah University, B.P 1796 Atlas-, Fez, Morocco

    Abderazzak Taime, Jamal Riffi, Abderrahim Saaidi & Khalid Satori

  2. LSI, Department of Mathematics, Physics and Computer Science Polydisciplinary Faculty of Taza, Dhar-Mahraz Sidi Mohamed Ben Abdellah University, B.P, 1223, Taza, Morocco

    Abderrahim Saaidi

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  1. Abderazzak Taime
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  2. Jamal Riffi
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  3. Abderrahim Saaidi
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  4. Khalid Satori
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Correspondence to Abderazzak Taime.

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Taime, A., Riffi, J., Saaidi, A. et al. Robust point matching via corresponding circles. Multimed Tools Appl 77, 15027–15046 (2018). https://doi.org/10.1007/s11042-017-5086-y

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  • DOI: https://doi.org/10.1007/s11042-017-5086-y

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