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Rapid object indexing and recognition using enhanced geometric hashing

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1064)

Abstract

We address the problem of 3D object recognition from a single 2D image using a model database. We develop a new method called enhanced geometric hashing. This approach allows us to solve for the indexing and the matching problem in one pass with linear complexity. Use of quasi-invariants allows us to index images in a new type of geometric hashing table. They include topological information of the observed objects inducing a high numerical stability.

We also introduce a more robust Hough transform based voting method, and thus obtain a fast and robust recognition algorithm that allows us to index images by their content. The method recognizes objects in the presence of noise and partial occlusion and we show that 3D objects can be recognized from any viewpoint if only a limited number of key views are available in the model database.

Keywords

  • Apparent Motion
  • Hash Table
  • Recognition Algorithm
  • Match Problem
  • Model Database

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.

This work was performed within a joint research programme between (in alphabetical order) Cnrs, Inpg, Inria, Ujf

References

  1. G. Bebis, M. Georgiopoulos, and N. da Vitoria Lobo. Learning geometric hashing functions for model-based object recognition. In Proceedings of the 5th International Conference on Computer Vision, Cambridge, Massachusetts, USA, pages 543–548. IEEE, June 1995.

    Google Scholar 

  2. J. Ben-Arie. The probabilistic peaking effect of viewed angles and distances with application to 3-D object recognition. Ieee Transactions on Pattern Analysis and Machine Intelligence, 12(8):760–774, August 1990.

    Google Scholar 

  3. T.O. Binford and T.S. Levitt. Quasi-invariants: Theory and exploitation. In Proceedings of Darpa Image Understanding Workshop, pages 819–829, 1993.

    Google Scholar 

  4. R.C. Bolles and R. Horaud. 3DPO: A three-Dimensional Part Orientation system. The International Journal of Robotics Research, 5(3):3–26, 1986.

    Google Scholar 

  5. A. Califano and R. Mohan. Multidimensional indexing for recognizing visual shapes. Ieee Transactions on Pattern Analysis and Machine Intelligence, 16(4):373–392, April 1994.

    Google Scholar 

  6. L. Grewe and A.C. Kak. Interactive learning of a multiple-attribute hash table classifier for fast object recognition. Computer Vision and Image Understanding, 61(3):387–416, May 1995.

    Google Scholar 

  7. W.E.L. Grimson, D.P. Huttenlocher, and D.W. Jacobs. A study of affine matching with bounded sensor error. In Proceedings of the 2nd European Conference on Computer Vision, Santa Margherita Ligure, Italy, pages 291–306, May 1992.

    Google Scholar 

  8. P. Gros. Using quasi-invariants for automatic model building and object recognition: an overview. In Proceedings of the NSF-ARPA Workshop on Object Representations in Computer Vision, New York, USA, December 1994.

    Google Scholar 

  9. P. Gros. Matching and clustering: Two steps towards object modelling in computer vision. The International Journal of Robotics Research, 14(5), October 1995.

    Google Scholar 

  10. R. Horaud and H. Sossa. Polyhedral object recognition by indexing. Pattern Recognition, 28(12):1855–1870, 1995.

    Google Scholar 

  11. Y. Lamdan and H.J. Wolfson. Geometric hashing: a general and efficient model-based recognition scheme. In Proceedings of the 2nd International Conference on Computer Vision, Tampa, Florida, USA, pages 238–249, 1988.

    Google Scholar 

  12. H. Murase and S.K. Nayar. Visual learning and recognition of 3D objects from appearance. International Journal of Computer Vision, 14:5–24, 1995.

    Google Scholar 

  13. R.P.N. Rao and D.H. Ballard. Object indexing using an iconic sparse distributed memory. In Proceedings of the 5th International Conference on Computer Vision, Cambridge, Massachusetts, USA, pages 24–31, 1995.

    Google Scholar 

  14. I. Shimshoni and J. Ponce. Probabilistic 3D object recognition. In Proceedings of the 5th International Conference on Computer Vision, Cambridge, Massachusetts, USA, pages 488–493, 1995.

    Google Scholar 

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© 1996 Springer-Verlag Berlin Heidelberg

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Lamiroy, B., Gros, P. (1996). Rapid object indexing and recognition using enhanced geometric hashing. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015523

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  • DOI: https://doi.org/10.1007/BFb0015523

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61122-6

  • Online ISBN: 978-3-540-49949-7

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