Advertisement

Learning of Shape Models from Exemplars of Biological Objects in Images

  • Petra PernerEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Generalized shape models of objects are necessary to match and identify an object in an image. Acquiring these kinds of models’ special methods is necessary as they allow learning the similarity pair-wise between the shapes. Their main concern is the establishment of point correspondences between two shapes and the detection of outlier. Known algorithm assume that the aligned shapes are quite similar in a way. But special problems arise if we align shapes that are very different, for example aligning concave to convex shapes. In such cases, it is indispensable to consider the order of the point-sets and to enforce legal sets of correspondences, otherwise the calculated distances are incorrect. We present our novel shape alignment algorithm which can also handle such cases. The algorithm establishes symmetric and legal one-to-one point correspondences between arbitrary shapes, represented as ordered sets of 2D-points and returns a distance measure which runs between 0 and 1.

Keywords

Shape alignment Correspondence problem Shape acquisition 

Notes

Acknowledgment

The project “Development of methods and techniques for the image-acquisition and computer-aided analysis of biologically dangerous substances BIOGEFA” is sponsored by the German Ministry of Economy BMWA under the grant number 16IN0147.

References

  1. 1.
    Thompson, D.A.: On Growth and Form. Cambridge University Press, Cambridge (1917)Google Scholar
  2. 2.
    Kendall, D.G.: A Survey of the Statistical Theory of Shape. Statistical Science 4(2), 87–120 (1989)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bookstein, F.L.: Size and shape spaces for landmark data in two dimensions. Stat. Sci. 1(2), 181–242 (1986)CrossRefGoogle Scholar
  4. 4.
    Bookstein, F.L.: Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med. Image Anal. 1(3), 225–244 (1997)CrossRefGoogle Scholar
  5. 5.
    Alon, J., Athitsos, V., Sclaroff, S.: Online and offline character recognition using alignment to prototypes. In: Proceedings of the 8th International Conference on Document Analysis and Recognition ICDAR 2005, IEEE Computer Society Press (2005)Google Scholar
  6. 6.
    Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)CrossRefGoogle Scholar
  7. 7.
    Alt, H., Guibas, L.J.: Discrete geometric shapes: matching, interpolation and approximation. In: Sack, J.-R., Urrutia, J. (eds.) Handbook of Computational Geometry, pp. 121–153. Elsevier Science Publishers B.V. (1996)Google Scholar
  8. 8.
    Rangarajan, A., Chui, H., Bookstein, F.L.: The softassign procrustes matching algorithm. In: Proceedings of Information Processing in Medical Imaging, pp. 29–42 (1997)Google Scholar
  9. 9.
    Sclaroff, S., Pentland, A.: Modal matching for correspondence and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 17(6), 545–561 (1995)CrossRefGoogle Scholar
  10. 10.
    Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. In: Proceedings of British Machine Vision Conference, Manchester, UK, vol. II, pp. 411–420 (2001)Google Scholar
  11. 11.
    Marte, O.-C., Marais, P.: Model-based segmentation of CT images. S. Afr. Comput. J. 28, 54–59 (2002)Google Scholar
  12. 12.
    Brett, A.D., Taylor, C.J.: A framework for automated landmark generation for automated 3D statistical model construction. In: Proceedings of Information Processing in Medical Imaging 1999, pp. 376–381 (1999)Google Scholar
  13. 13.
    Feldmar, J., Ayache, N.: Rigid, affine and locally affine registration of free-form surfaces. Int. J. Comput. Vis. 18(3), 99–119 (1996)CrossRefGoogle Scholar
  14. 14.
    Hill, A., Taylor, C.J., Brett, A.D.: A framework for automatic landmark identification using a new method of nonrigid correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 241–251 (2000)CrossRefGoogle Scholar
  15. 15.
    Veltkamp, R.C.: Shape matching: similarity measures and algorithms. In: Shape Modelling International, pp. 188–197 (2001)Google Scholar
  16. 16.
    Lele, S.R., Richtsmeier, J.T.: An Invariant Approach to Statistical Analysis of Shapes. Chapman & Hall/CRC, Boca Raton (2001)CrossRefGoogle Scholar
  17. 17.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley, Chichester (1998)zbMATHGoogle Scholar
  18. 18.
    Besl, P., McKay, N.: A method for registration of 3-D shapes, IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  19. 19.
    Aksenov, P., Clark, I., Grant, D., Inman, A., Vartikovski, L., Nebel, J.-C.: 3D thermography for quantification of heat generation resulting from inflammation. In: Proceedings of 8th 3D Modelling Symposium, Paris, France (2003)Google Scholar
  20. 20.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–522 (2002)CrossRefGoogle Scholar
  21. 21.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. In: 1st International Conference on Computer Vision, London, pp. 259–268 (1987)Google Scholar
  22. 22.
    Cheng, D.-C., Schmidt-Trucksäss, A., Cheng, K.-S., Burkhardt, H.: Using Snakes to Detect the Intimal and Aventitial Layers of the Common Carotid Artery Wall in Sonographic Images. Comput. Methods Programs Biomed. 67, 27–37 (2002)CrossRefGoogle Scholar
  23. 23.
    Cootes, T.F., Taylor, C.J.: A mixture model for representing shape variation. Image Vis. Comput. 17(8), 567–574 (1999)CrossRefGoogle Scholar
  24. 24.
    Mortensen, E.N., Barrett, W.A.: Intelligent scissors for image composition. In: Computer Graphics Proceedings, pp. 191–198 (1995)Google Scholar
  25. 25.
    Haenselmann, T., Effelsberg, W.: Wavelet-based semi-automatic live-wire segmentation. In: Proceedings of the SPIE Human Vision and Electronic Imaging VII, vol. 4662, pp. 260–269 (2003)Google Scholar
  26. 26.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRefGoogle Scholar
  27. 27.
    Wall, K., Daniellson, P.-E.: A fast sequential method for polygonal approximation of digitized curves. Comput. Graph. Image Process. 28, 220–227 (1984)CrossRefGoogle Scholar
  28. 28.
    Perner, P., Jänichen, S.: Learning of form models from exemplars. In: Fred, A., Caelli, T., Duin, R.P.W., Campilho, A., de Ridder, D. (eds.) Structural, Syntactic, and Statistical Pattern Recognition, Proceedings of the SSPR 2004, LNCS 3138, p. 153. Springer Verlag, Lisbon/Portugal (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Computer Vision and Applied Computer Sciences, IBaILeipzigGermany

Personalised recommendations