Fast segmentation of range images

  • Michal Haindl
  • Pavel Žid
Session 3: Segmentation & Coding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

A new type of range image segmentation method is introduced. The image segmentation is based on a recursive adaptive regression model prediction for detecting range image step discontinuities which are present at object face borders. Border pixels are detected in two perpendicular directions and detection results are combined together. Two predictors in each direction use identical contextual information from the pixel's neighbourhood and they mutually compete for the most optimal discontinuity detection. The method suggested can be successfully applied also to other image segmentation applications, e.g. panchromatic or multispectral image data, etc.

Keywords

Segmentation Result Range Image Multispectral Image Laser Range Finder Polyhedral Object 
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.

References

  1. 1.
    Besl, P.J., Jain, R.C.: Three-dimensional object recognition. ACM Computing Surveys 17 (1985) no.1 75–145Google Scholar
  2. 2.
    Besl, P.J., Jain, R.C.: Segmentation Through Variable-Order Surface Fitting. IEEE Trans. PAMI 10 (1988) no.2 167–192Google Scholar
  3. 3.
    Broemeling,L.D.: Bayesian Analysis of Linear Models. New York, Dekker 1985Google Scholar
  4. 4.
    Flynn, P.J. Jain,A.K.: BONSAI: 3D Object Recognition Using Constrained Search. IEEE Trans. PAMI 13 (1991) no.10 1066–1075Google Scholar
  5. 5.
    Haindl,M., Šimberová, S.: A Multispectral Image Line Reconstruction method. In: Theory & Applications of Image Analysis. P. Johansen, S. Olsen Eds., World Scientific Publishing Co., Singapore, 1992Google Scholar
  6. 6.
    Hoffman, R.L., Jain, A.K.: Segmentation and Classification of Range Images. IEEE Trans. PAMI 9 (1987) no.5 608–620Google Scholar
  7. 7.
    Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldof, D.B., Bowyer, K., Eggert, D.W., Fitzgibbon, A., Fisher, R.B.: An Experimental Comparison of Range Image Segmentation Algorithms. IEEE Trans. PAMI 18 (1996) no.7 673–689Google Scholar
  8. 8.
    Jiang, X.Y., Bunke, H.: Fast Segmentation of Range Images into Planar Regions by Scan Line Grouping. Machine Vision and App. 7 (1994) no. 2 115–122Google Scholar
  9. 9.
    Perceptron Inc., LASAR Hardware Manual, 23855 Research Drive, Farmington Hills, Michigan, 1993Google Scholar
  10. 10.
    Sinha, S.S., Jain, R.: Handbook of Pattern Recognition and Image Processing. Wiley, New York, 1994Google Scholar
  11. 11.
    Stahs, T.G., Wahl, F.M.: Fast and Robust Data Acquisition in a Low-Cost Environment., SPIE no.1395: Close-Range Photogrammetry Meets Machine Vision (1990) 496–503, ZurichGoogle Scholar
  12. 12.
    Zhang, X.; Zhao, D.: Range image segmentation via edges and critical points. Proc. SPIE 2501 (1995) no. 3 1626–1637Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Michal Haindl
    • 1
    • 2
  • Pavel Žid
    • 2
  1. 1.Center for Machine PerceptionCzech Technical UniversityPrahaCzech Republic
  2. 2.Institute of Information Theory and Automation of the Czech Academy of SciencesPrahaCzech Republic

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