Shape Similarity and Visual Parts

  • Longin Jan Latecki
  • Rolf Lakämper
  • Diedrich Wolter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2886)


Human perception of shape is based on visual parts of objects to a point that a single, significant visual part is sufficient to recognize the whole object. For example, if you see a hand in the door, you expect a human behind the door. Therefore, a cognitively motivated shape similarity measure for recognition applications should be based on visual parts. This cognitive assumption leads to two related problems of scale selection and subpart selection. To find a given query part Q as part of an object C, Q needs to have a correct size with regards to C (scale selection). Assuming that the correct size is selected, the part Q must be compared to all possible subparts of C (subpart selection). For global, contour-based similarity measures, scaling the whole contour curves of both objects to the same length usually solves the problem of scale selection. Although this is not an optimal solution, it works if the whole contour curves are ‘sufficiently’ similar. Subpart selection problem does not occur in the implementation of global similarity measures.

In this paper we present a shape similarity system that is based on correspondence of visual parts, and apply it to robot localization and mapping. This is a particularly interesting application, since the scale selection problem does not occur here and visual parts can be obtained in a very simple way. Therefore, only the problem of subpart selection needs to be solved. Our solution to this problem is based on a contour based shape similarity measure supplemented by a structural arrangement information of visual parts.


Mobile Robot Shape Descriptor Visual Part Zernike Moment Shape Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Arkin, M., Chew, L.P., Huttenlocher, D.P., Kedem, K., Mitchell, J.S.B.: An efficiently computable metric for comparing polygonal shapes. IEEE Trans. PAMI 13, 206–209 (1991)Google Scholar
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Blum, H.: Biological shape and visual science. Journal of Theor. Biol. 38, 205–287 (1973)CrossRefGoogle Scholar
  4. 4.
    da, L., Costa, F., Cesar, R.M.: Shape Analysis and Classification. In: Theory and Practice. CRC Press, Boca Raton (2001)Google Scholar
  5. 5.
    Cox, I.J.: Blanche – An experiment in Guidance and Navigation of an Autonomous Robot Vehicle. IEEE Transaction on Robotics and Automation 7(2), 193–204 (1991)CrossRefGoogle Scholar
  6. 6.
    DeMenthon, D.F., Latecki, L.J., Rosenfeld, A., Vuilleumier Stückelberg, M.: Relevance ranking and smart fast-forward of video data by polygon simplification. pp. 49–61 (2000)Google Scholar
  7. 7.
    Dissanayake, G., Durrant-Whyte, H., Bailey, T.: A computationally efficient solution to the simultaneous localization and map building (SLAM) problem. In: ICRA 2000 Workshop on Mobile Robot Navigation and Mapping (2000)Google Scholar
  8. 8.
    Gutmann, J.-S., Schlegel, C.: AMOS: Comparison of Scan Matching Approaches for Self-Localization in Indoor Environments. In: 1st Euromicro Workshop on Advanced Mobile Robots, Eurobot (1996)Google Scholar
  9. 9.
    Gutmann, J.-S., Konolige, K.: Incremental Mapping of Large Cyclic Environments. In: Int. Symposium on Computational Intelligence in Robotics and Automation (CIRA 1999), Monterey (1999)Google Scholar
  10. 10.
    Gutmann, J.-S.: Robuste Navigation mobiler System, PhD thesis, University of Freiburg, Germany (2000)Google Scholar
  11. 11.
    Hähnel, D., Schulz, D., Burgard, W.: Map Building with Mobile Robots in Populated Environments. In: Int. Conf. on Int. Robots and Systems, IROS (2002)Google Scholar
  12. 12.
    Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the Hausdorff distance. IEEE Trans. PAMI 15, 850–863 (1993)Google Scholar
  13. 13.
    Khotanzan, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Trans. PAMI 12, 489–497 (1990)Google Scholar
  14. 14.
    Kuipers, B.: The Spatial Semantic Hierarchy. Artificial Intelligence 119, 191–233 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Latecki, L.J., de Wildt, D.: Automatic recognition of unpredictable events in videos. In: Proc. of Int. Conf. on Pattern Recognition (ICPR), vol. 2, Quebec City (August 2002)Google Scholar
  16. 16.
    Latecki, L.J., Lakämper, R.: Convexity rule for shape decomposition based on discrete contour evolution. Computer Vision and Image Understanding 73, 441–454 (1999)CrossRefGoogle Scholar
  17. 17.
    Latecki, L.J., Lakämper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10), 1185–1190 (2000)CrossRefGoogle Scholar
  18. 18.
    Latecki, L.J., Lakämper, R.: Application of planar shapes comparison to object retrieval in image databases. Pattern Recognition 35(1), 15–29 (2002)zbMATHCrossRefGoogle Scholar
  19. 19.
    Latecki, L.J., Lakämper, R.: Polygon evolution by vertex deletion. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, p. 398. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 424–429, South Carolina (June 2000)Google Scholar
  21. 21.
    Lu, F., Milios, E.: Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems 18(3), 249–275 (1997)CrossRefGoogle Scholar
  22. 22.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. In: Smeulders, A.W.M., Jain, R. (eds.) Image Databases and Multi-Media Search, pp. 51–58. World Scientific Publishing, Singapore (1997)Google Scholar
  23. 23.
    Mokhtarian, F., Mackworth, A.K.: A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. PAMI 14, 789–805 (1992)Google Scholar
  24. 24.
    Röfer, T.: Using Histogram Correlation to Create Consistent Laser Scan Maps. In: IEEE Int. Conf. on Robotics Systems (IROS). EPFL, Lausanne, Switzerland, pp. 625– 630 (2002)Google Scholar
  25. 25.
    Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. Int. J. of Computer Vision 35, 13–32 (1999)CrossRefGoogle Scholar
  26. 26.
    Thrun, S.: Learning Metric-Topological Maps for Indoor Mobile Robot Navigation. Artificial Intelligence 99, 21–71 (1998)zbMATHCrossRefGoogle Scholar
  27. 27.
    Thrun, S.: Probabilistic algorithms in robotics. AI Magazine 21(4), 93–109 (2000)Google Scholar
  28. 28.
    Thrun, S.: Robot Mapping: A Survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, San Francisco (2002)Google Scholar
  29. 29.
    Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In: IEEE Int. Conf. on Robotics and Automation, ICRA (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Longin Jan Latecki
    • 1
  • Rolf Lakämper
    • 1
  • Diedrich Wolter
    • 2
  1. 1.Dept. of Computer and Information SciencesTemple UniversityPhiladelphiaUSA
  2. 2.Dept. of Computer ScienceUniversity of BremenBremenGermany

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