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Image Registration pp 267–312Cite as

Point Pattern Matching

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Various point pattern matching algorithms are reviewed and compared. Among the matching algorithms discussed are random sample and consensus (RANSAC), graph-based, feature-based, clustering-based, invariance-based, axis of minimum inertia-based, relaxation-based, and spectral graph theory-based algorithms. To speed up the matching process, the coarse-to-fine search strategy is also discussed and its use in matching of point patterns with nonlinear geometric differences is demonstrated. Also included in this chapter are detailed matching algorithms and methods to determine their performances.

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References

  1. Ahuja, N.: Dot pattern processing using Voronoi neighborhoods. IEEE Trans. Pattern Anal. Mach. Intell. 4(3), 336–343 (1982)

    Article  MathSciNet  Google Scholar 

  2. Bolles, R.C.: Robust feature matching through maximal cliques. In: SPIE Conf. Imaging Applications for Automated Industrial Inspection and Assembly, vol. 182, pp. 140–149 (1979)

    Chapter  Google Scholar 

  3. Bowyer, A.: Computing Dirichlet tessellations. Comput. J. 24(2), 162–166 (1981)

    Article  MathSciNet  Google Scholar 

  4. Bron, C., Kerbosch, J.: Algorithm 547: Finding all cliques of an undirected graph. Commun. ACM 16(9), 575–577 (1973)

    Article  MATH  Google Scholar 

  5. Capel, D.: An effective bail-out test for RANSAC consensus scoring. In: Proc. British Machine Vision Conf., pp. 629–638 (2005)

    Google Scholar 

  6. Carcassoni, M., Hancock, E.R.: Point pattern matching with robust spectral correspondence. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 649–655 (2000)

    Google Scholar 

  7. Carcassoni, M., Hancock, E.R.: Spectral correspondence for point pattern matching. Pattern Recognit. 36, 193–204 (2003)

    Article  MATH  Google Scholar 

  8. Chang, S.-H., Cheng, F.-H., Hsu, W.-H., Wu, G.-Z.: Fast algorithm for point pattern matching: Invariant to translations, rotations, and scale changes. Pattern Recognit. 30(2), 311–320 (1997)

    Article  Google Scholar 

  9. Cheng, F.-H.: Point pattern matching algorithm invariant to geometrical transformation and distortion. Pattern Recognit. Lett. 17, 1429–1435 (1996)

    Article  MATH  Google Scholar 

  10. Choi, O., Kweon, I.S.: Robust feature point matching by preserving local geometric consistency. Comput. Vis. Image Underst. 113, 726–742 (2009)

    Article  Google Scholar 

  11. Chum, O., Matas, J.: Matching with PROSAC–progressive sample consensus. In: Proc. Computer Vision and Pattern Recognition, vol. 1, pp. 220–226 (2005)

    Google Scholar 

  12. Chum, O., Matas, J.: Optimal randomized RANSAC. IEEE Trans. Pattern Anal. Mach. Intell. 30(8), 1472–1482 (2008)

    Article  Google Scholar 

  13. Chung, F.R.K.: Spectral Graph Theory, 2nd edn., pp. 1–22. Am. Math. Soc., Providence (1997)

    MATH  Google Scholar 

  14. Coelho, C., Heller, A., Mundy, J.L., Forsyth, D.A., Zisserman, A.: An experimental evaluation of projective invariants. In: Mundy, J.L., Zisserman, A. (eds.) Geometric Invariance in Computer Vision, pp. 87–104. The MIT Press, Cambridge (1992)

    Google Scholar 

  15. Denton, J.A., Beveridge, J.R.: An algorithm for projective point matching in the presence of spurious points. Pattern Recognit. 40, 586–595 (2007)

    Article  MATH  Google Scholar 

  16. Dwyer, R.A.: A faster divide-and-conquer algorithm for constructing Delaunay triangulations. Algorithmica 2, 137–151 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  17. Fang, T.J., Huang, Z.H., Kanal, L.N., Lavine, B.D., Stockman, G., Xiong, F.L.: Three-dimensional object recognition using a transform clustering technique. In: Proc. 6th Int’l Conf. Pattern Recognition, pp. 678–681 (1982)

    Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  19. Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, New York (2002)

    Google Scholar 

  20. Goshtasby, A., Page, C.V.: Image matching by a probabilistic relaxation labeling process. In: Proc. 7th Int’l Conf. Pattern Recognition, vol. 1, pp. 307–309 (1984)

    Google Scholar 

  21. Goshtasby, A., Stockman, G.C.: Point pattern matching using convex hull edges. IEEE Trans. Syst. Man Cybern. 15(5), 631–637 (1985)

    Article  Google Scholar 

  22. Grimson, W.E.L., Huttenlocher, D.P.: On the sensitivity of geometric hashing. In: Proc. 3rd Int’l Conf. Computer Vision, pp. 334–338 (1990)

    Google Scholar 

  23. Hong, J., Tan, X.: A new approach to point pattern matching. In: Proc. 9th Int’l Conf. Pattern Recognition, vol. 1, pp. 82–84 (1988)

    Google Scholar 

  24. Hsiao, E., Collet, A., Hebert, M.: Making specific features less discriminative to improve point-based 3D object recognition. In: Int’l Conf. Computer Vision and Pattern Recognition, pp. 2653–2660 (2010)

    Google Scholar 

  25. Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)

    Article  Google Scholar 

  26. Lamdan, Y., Wolfson, H.J.: Geometric hashing: A general and efficient model-based recognition scheme. In: Proc. 2nd Int’l Conf. Computer Vision, pp. 238–249 (1988)

    Google Scholar 

  27. Lamdan, Y., Wolfson, H.J.: On the error analysis of geometric hashing. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 22–27 (1991)

    Google Scholar 

  28. Lavine, D., Lambird, B.A., Kanal, L.N.: Recognition of spatial point patterns. Pattern Recognit. 16(3), 289–295 (1983)

    Article  MATH  Google Scholar 

  29. Lee, J.-H., Won, C.-H.: Topology preserving relaxation labeling for nonrigid point matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 427–432 (2011)

    Article  Google Scholar 

  30. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: Proc. Int’l Conf. Computer Vision, vol. 2, pp. 1482–1489 (2005)

    Google Scholar 

  31. Matas, J., Chum, O.: Randomized RANSAC with Td:d test. Image Vis. Comput. 22(10), 837–842 (2004)

    Article  Google Scholar 

  32. Mundy, J.L., Zisserman, A.: Geometric Invariance in Computer Vision. The MIT Press, Cambridge (1992)

    Google Scholar 

  33. Nister, D.: Preemptive RANSAC for live structure and motion estimation. In: Proc. Int’l Conf. Computer Vision, Oct., vol. 1, pp. 199–206 (2003)

    Chapter  Google Scholar 

  34. Ogawa, H.: Labeled point pattern matching by fuzzy relaxation. Pattern Recognit. 17(5), 569–573 (1984)

    Article  Google Scholar 

  35. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)

    MATH  Google Scholar 

  36. Pilu, M.: A direct method for stereo correspondence based on singular value decomposition. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 261–266 (1997)

    Google Scholar 

  37. Rabin, J., Delon, J., Gousseau, Y.: A statistical approach to the matching of local features. SIAM J. Imaging Sci. 2(3), 931–958 (2008)

    Article  MathSciNet  Google Scholar 

  38. Ranade, S., Rosenfeld, A.: Point pattern matching by relaxation. Pattern Recognit. 12(4), 269–275 (1980)

    Article  Google Scholar 

  39. Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Trans. Syst. Man Cybern. 6(6), 420–433 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  40. Sclaroff, S., Pentland, A.P.: Model matching for correspondence and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 17(6), 545–561 (1995)

    Article  Google Scholar 

  41. Scott, G.L., Longuet-Higgins, H.C.: An algorithm for associating the features of two images. Proc. R. Soc. Lond. B 244, 21–26 (1991)

    Article  Google Scholar 

  42. Serradell, E., Özuysal, M., Lepetit, V., Fua, P., Moreno-Noguer, F.: Combining geometric and appearance priors for robust homography estimation. In: Proc. European Conf. Computer Vision (2010)

    Google Scholar 

  43. Shapiro, L.S., Brady, J.M.: Feature-based correspondence: An eigenvector approach. Image Vis. Comput. 10(5), 283–288 (1992)

    Article  Google Scholar 

  44. Stewart, G.W.: On the early history of the singular value decomposition. SIAM Rev. 35(4), 551–566 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  45. Stockman, G.: Object recognition and localization via pose clustering. Comput. Vis. Graph. Image Process. 40, 361–387 (1987)

    Article  Google Scholar 

  46. Stockman, G., Esteva, J.C.: Use of geometrical constraints and clustering to determine 3-D object pose. In: Proc. 7th Int’l Conf. Pattern Recognition, vol. 2, pp. 742–744 (1984)

    Google Scholar 

  47. Stockman, G., Kopstein, S., Benett, S.: Matching images to models for registration and object detection via clustering. IEEE Trans. Pattern Anal. Mach. Intell. 4(3), 229–241 (1982)

    Article  Google Scholar 

  48. Tordoff, B., Murray, D.W.: Guided sampling and consensus for motion estimation. In: Proc. European Conf. Computer Vision, pp. 82–98 (2002)

    Google Scholar 

  49. Torr, P., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. In: Computer Vision and Image Understanding, pp. 138–156 (2000)

    Google Scholar 

  50. Umeyama, S.: An eigendecomposition approach to weighted graph matching problems. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 695–703 (1988)

    Article  MATH  Google Scholar 

  51. van Wamelen, P.B., Li, Z., Iyengar, S.S.: A fast expected time algorithm for the 2-D point pattern matching problem. Pattern Recognit. 37, 1699–1711 (2004)

    Article  Google Scholar 

  52. Wang, W.-H., Chen, Y.-C.: Point pattern matching by line segments and labels. Electron. Lett. 33(6), 478–479 (1997)

    Article  Google Scholar 

  53. Wang, H., Hancock, E.R.: A kernel view of spectral point pattern matching. In: Proc. IAPR Int’l Workshop Structural, Syntactic, and Statistical Pattern Recognition, pp. 361–369 (2004)

    Chapter  Google Scholar 

  54. Wolfson, H.J., Rigoutsos, I.: Geometric hashing: An overview. In: IEEE Computational Science & Engineering, Oct.–Dec., pp. 10–21 (1997)

    Google Scholar 

  55. Wu, Z., Goshtasby, A.: A subdivision approach to image registration. Technical Report, Intelligent Systems Laboratory, Department of Computer Science and Engineering, Wright State University, January 2011

    Google Scholar 

  56. Zhan, C.T. Jr.: An algorithm for noisy template matching. In: Information Processing 74, pp. 698–701. North-Holland, Amsterdam (1974)

    Google Scholar 

  57. Zhang, W., Kos̆ecká, J.: Generalized RANSAC framework for relaxed correspondence problems. In: Proc. Int’l Sym. 3D Data Processing, Visualization, and Transmission (2006)

    Google Scholar 

  58. Zhao, Z., Liu, H.: Searching for interacting features. In: Proc. Int’l J. Conf. Artificial Intelligence (IJCAI), January (2007)

    Google Scholar 

  59. Zhao, J., Zhou, S., Sun, J., Li, Z.: Point pattern matching using relative shape context and relaxation labeling. In: Proc. 2nd Int’l Conf. Advanced Computer Control (ICACC), vol. 5, pp. 516–520 (2010)

    Google Scholar 

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Authors and Affiliations

  1. Dept. Computer Science and Engineering, 303 Russ Engineering Center, Wright State University, Dayton, OH, USA

    Prof. A. Ardeshir Goshtasby

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  1. Prof. A. Ardeshir Goshtasby
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Correspondence to A. Ardeshir Goshtasby .

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Goshtasby, A.A. (2012). Point Pattern Matching. In: Image Registration. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2458-0_7

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  • DOI: https://doi.org/10.1007/978-1-4471-2458-0_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2457-3

  • Online ISBN: 978-1-4471-2458-0

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