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Better Surface Intersections by Constrained Evolution

  • C. Robertson
  • R. B. Fisher

Abstract

Reverse-engineering a machined part to generate a CAD model requires range data to be collected and registered from many views then segmented into surface primitives. Correctly computing the intersections of these surface primitives is a critical part of building the CAD model. We describe a method for correcting the ragged boundaries often found with region-growing algorithms and show examples of its application. This method is useful for a large subset of the surface intersections that regularly appear in manufactured objects.

Keywords

Surface Intersection Sphere Centre Boundary Position Domain Constraint Tangent Continuity 
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.

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References

  1. 1.
    Besl, P.J. (1986) Surfaces in Early Range Image Understanding. PhD Dissertation, Electrical Engineering and Computer Science Department (RSD-TR-10-86), University of Michigan.Google Scholar
  2. 2.
    Hoover,A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, K., Goldgof, D., Bowyer, K., Eggert, D., Fitzgibbon, A., Fisher, R. (1996) “An Experimental Comparison of Range Segmentation Algorithms”, IEEE Trans. Pat. Anal, and Mach. Intel., 18(7), pp 673–689.CrossRefGoogle Scholar
  3. 3.
    Faux, I.D. and Pratt, M.J. (1985) Computational Geometry for Design and Manufacture, Ellis Horwood Limited, Chichester, UK.Google Scholar
  4. 4.
    Fisher, R.B.(1997), Fitzgibbon, A., Eggert, D., “Extracting Surface Patches from Complete Range Descriptions”, Proc. Int. Conf. on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Canada, pp 148–155.Google Scholar
  5. 5.
    Michalewicz, Z.,(1996) Genetic Algorithms +h Data Structures = Evolution Programs, Third Edition, Springer.Google Scholar
  6. 6.
    Michalewicz, Z. (1996), Dasgupta D., Le Riche R.G., and Schoenauer, M., “Evolutionary algorithms for constrained engineering problems”, special issue on Genetic Algorithms and Industrial Engineering, ed. M. Gen, G.S. Wasserman and A. E. Smith, International Journal of Computers and Industrial Engineering.Google Scholar
  7. 7.
    Robertson, C. (1999), Fisher, R.B., Werghi, N., Ashbrook, A., “An Improved Algorithm to Extract Surfaces from Complete Range Descriptions”, Proc. World Manuf. Conf, WMC’99 (ISMT′99), pp 592–598, ICSC Academic Press, Durham.Google Scholar
  8. 8.
    Robertson, C. (1998), Come, D., Fisher, R.B., Werghi, N., Ashbrook A., “Investigating Evolutionary Optimisation of Constrained Functions to Capture Shape Descriptions from Range Data”, Proc. 3rd On-line World Conference on Soft Computing (WSC3) (see http://www.cranfield.ac.uk/wsc3/ also in Advances in Soft Computing — Engineering Design and Manufacturing, eds. Roy, Furuhashi and Chawdhry, Springer-Verlag, 1998.
  9. 9.
    Robertson, C. (1999), Fisher, R.B., Werghi, N., Ashbrook, A., “An Evolutionary Approach to Fitting Constrained Degenerate Second Order Surfaces”, to be published in the Proceedings of EvoIASP′99, Sweden (28th May 1999), eds. Poli, Voigt, Cagnoni, Corne, Smith and Fogarty, Springer-Verlag Berlin.Google Scholar
  10. 10.
    Bolles, R. C and Fischler, M. A (1980), “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Technical Note 213, Artificial Intelligence Center, SRI International, Menlo Park, California.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  1. 1.Vision Group, Institute for Perception, Action and Behaviour Division of InformaticsUniversity of EdinburghEdinburghUK

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