Model Selection for Range Segmentation of Curved Objects

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)


In the present paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. A novel criterion for surface (model) selection is introduced and its performance for selecting the underlying model of various surfaces has been tested and compared with many other existing techniques. Using this criterion, we then present a range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The algorithm simultaneously identifies the type (order and geometric shape) of surface and separates all the points that are part of that surface from the rest in a range image. The paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces.


Curve Surface Segmentation Algorithm Segmentation Result Machine Intelligence Range Data 
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.
    Arman, F., Aggarwal, J.: Model-Based Object Recognition in Dense Range Images. ACM Computing Surveys 25, 5–13 (1993)CrossRefGoogle Scholar
  2. 2.
    Bab-Hadiashar, A., Suter, D.: Robust Segmentation of Visual Data Using Ranked Unbiased Scale Estimate, ROBOTICA. International Journal of Information, Education and Research in Robotics and Artificial Intelligence 17, 649–660 (1999)Google Scholar
  3. 3.
    Besl, P.J., Jain, R.C.: Segmentation Through Variable-Order Surface Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 167–192 (1988)CrossRefGoogle Scholar
  4. 4.
    Boyer, K.L., Mirza, M.J., Ganguly, G.: The Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data. IEEE Transaction on Pattern Analysis and Machine Intelligence 16, 987–1001 (1994)CrossRefGoogle Scholar
  5. 5.
    Bozdogan, H.: Model Selection and Akaike’s Information Criterion (AIC): The General Theory and Its Analytical Extensions. Psychometrica 52, 345–370 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Chickering, D., Heckerman, D.: Efficient Approximation for the Marginal Likelihood of Bayesian Networks with Hidden Variables. Machine Learning 29(2-3), 181–212 (1997)zbMATHCrossRefGoogle Scholar
  7. 7.
    Duncan, J.S., Lee, F.A., Smeulders, A.W.M., Zaret, B.L.: A Bending Energy Model for Measurement of Cardic Shape Deformity. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 307–319 (1991)Google Scholar
  8. 8.
    Fan, T.J., Medioni, G., Nevatia, R.: Segmented Descriptions of 3-D Surfaces. IEEE Trans. on Robotics and Automation 3, 527–538 (1987)CrossRefGoogle Scholar
  9. 9.
    Fitzgibbon, A.W., Eggert, D.W., Fisher, R.B.: In: High-Level CAD Model Acquisition From Range Images, High-Level CAD Model Acquisition From Range Images, Dept of Artificial Intelligence, Univ. of Edinburgh (1995)Google Scholar
  10. 10.
    Goldgof, D.B., Huang, T.S., Lee, H.: A Curvature-Based Approach to Terrain Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 1213–1217 (1989)CrossRefGoogle Scholar
  11. 11.
    Hoffman, R.L., Jain, A.K.: Segmentation and Classification of Range Images. IEEE PAMI 9, 608–620 (1987)Google Scholar
  12. 12.
  13. 13.
    Hoover, A., Jean-Baptist, G., Jiang, X.: An Experimental Comparison of Range Image Segmentation Algorithms. IEEE Transaction on Pattern Analysis and Machine Recognition 18, 673–689 (1996)CrossRefGoogle Scholar
  14. 14.
    Jiang, X., Bunke, H.: Range Image Segmentation: Adaptive Grouping of Edges into Region. In: Proceedings of Asian Conference on Computer Vision, Hong Kong, pp. 299–306 (1998)Google Scholar
  15. 15.
    Kanatani, K.: What Is the Geometric AIC? Reply to My Reviewers (1987) (unpublished)Google Scholar
  16. 16.
    Kanatani, K.: Model Selection for Geometric Inference. In: The 5th Asian Conference on Computer Vision, Melbourne, Australia, pp. xxi–xxxii (January 2002)Google Scholar
  17. 17.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. In: Anonymous, pp. 269–276 (1987)Google Scholar
  18. 18.
    Lee, K.-M., Meer, P., Park, R.-H.: Robust Adaptive Segmentation of Range Images. IEEE Trans. Pattern Anal. Machine Intell., 200–205 (1998)Google Scholar
  19. 19.
    Mallows, C.L.: Some Comments on CP. Technometrics 15, 661–675 (1973)zbMATHCrossRefGoogle Scholar
  20. 20.
    Marshall, D., Lukacs, G., Martin, R.: Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 304–314 (2001)CrossRefGoogle Scholar
  21. 21.
    Meer, P., Mintz, D., Rosenfeld, A.: Least Median of Squares based robust analysis of Image Structure. In: Proce. DARPA Image Understanding Workshop, Pittsburgh, PA, September 1990, pp. 231–254 (1990)Google Scholar
  22. 22.
    Newman, T.S., Flynn, P.J., Jain, A.K.: Model-based Classification of Quadric Surfaces. CVGIP: Image Understanding 58, 235–249 (1993)CrossRefGoogle Scholar
  23. 23.
    Powell, M.W., Bower, K., Jiang, X., Bunke, H.: Comparing Curved-Surface Range Image Segmentors. In: Proc. of 6th International Conference on Computer Vision (ICCV), Bombay, India, pp. 286–291 (1998)Google Scholar
  24. 24.
    Rissanen, J.: Universal Coding, Information, Prediction and Estimation. IEEE Trans. Inf. Theory 30, 629–636 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Rissanen, J.: Modeling by Shortest Data Description. Automatica 14, 465–471 (1978)zbMATHCrossRefGoogle Scholar
  26. 26.
    Rousseeuw, P.J.: Least median of squares. Journal of the American Satistical Association 85, 115–119 (1984)Google Scholar
  27. 27.
    Timoshenko, P.S., Krieger, S.W.: Theory of Plates and Shells. In: Timoshenko, P.S., Krieger, S.W. (eds.) Pure Bending of Plates, ch. 2, p. 4647. McGraw-Hill, New York (1959)Google Scholar
  28. 28.
    Trucco, E., Fisher, R.B.: Experiments in Curvature-based Segmentation of Range Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 3177182 (1995)CrossRefGoogle Scholar
  29. 29.
    Van Vliet, J.L., Verbeek, W.P.: Curvature and Bending Energy in Digitized 2D and 3D Images. In: Proceedings of 8th Scandinavian Conference on Image Analysis, Toromso, Norway, pp. 1403–1410 (1993)Google Scholar
  30. 30.
    Young, I.T., Walker, J.E., Bowie, J.E.: An analysis technique for biological shape. I. Information Control 25, 357–370 (1974)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  1. 1.School of Engineering & ScienceSwinburne University of TechnologyMelbourneAustralia

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