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Learning appearance models for object recognition

  • Appearance-Based Representations
  • Conference paper
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Object Representation in Computer Vision II (ORCV 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1144))

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Abstract

We describe how to model the appearance of an object using multiple views, learn such a model from training images, and recognize objects with it. The model uses probability distributions to characterize the significance, position, and intrinsic measurements of various discrete features of appearance; it also describes topological relations among features. The features and their distributions are learned from training images depicting the modeled object. A matching procedure, combining qualities of both alignment and graph subisomorphism methods, uses feature uncertainty information recorded by the model to guide the search for a match between model and image. Experiments show the method capable of learning to recognize complex objects in cluttered images, acquiring models that represent those objects using relatively few views.

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References

  1. N. Ayache, O.D. Faugeras. HYPER: A new approach for the recognition and positioning of two-dimensional objects. IEEE Trans. Patt. Anal. Mach. Intell. PAMI-8:44–54, 1986.

    Google Scholar 

  2. J.S. Beis, D.G. Lowe. Learning indexing functions for 3-D model-based object recognition. In Proc. Conf. Computer Vision and Patt. Recognit., 275–280, 1994.

    Google Scholar 

  3. G.J. Bierman. Factorization Methods for Discrete Sequential Estimation. Academic Press, 1977.

    Google Scholar 

  4. T.M. Breuel. Fast recognition using adaptive subdivisions of transformation space. In Proc. Conf. Computer Vision and Patt. Recognit., 445–451, 1992.

    Google Scholar 

  5. J.B. Burns, E.M. Riseman. Matching complex images to multiple 3D objects using view description networks. In Proc. Conf. Computer Vision and Patt. Recognit., 328–334, 1992.

    Google Scholar 

  6. O.I. Camps, L.G. Shapiro, R.M. Haralick. Object recognition using prediction and probabilistic matching. In Proc. of the IEEE/RSJ Int. Conf. on Intell. Robots and Systems, 1044–1052, July 1992.

    Google Scholar 

  7. T. A. Cass. Polynomial-time object recognition in the presence of clutter, occlusion, and uncertainty. In Proc. European Conf. on Computer Vision, 834–842, 1992.

    Google Scholar 

  8. C.H. Chen, P.G. Mulgaonkar. Automatic vision programming. CVGIP: Image Understanding 55:170–183, 1992.

    Google Scholar 

  9. J.H. Connell, M. Brady. Generating and generalizing models of visual objects. Artificial Intell. 31:159–183, 1987.

    Google Scholar 

  10. D.H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning 2:139–172, 1987.

    Google Scholar 

  11. P. Gros. Matching and clustering: Two steps towards automatic object model generation in computer vision. In Proc. AAAI Fall Symp.: Machine Learning in Computer Vision, AAAI Press, 1993.

    Google Scholar 

  12. Y. Hel-Or, M. Werman. Pose estimation by fusing noisy data of different dimensions. IEEE Trans. Patt. Anal. Mach. Intell. 17:195–201, 1995.

    Google Scholar 

  13. D.G. Lowe. The viewpoint consistency constraint. Int. J. Computer Vision 1:57–72, 1987.

    Google Scholar 

  14. H. Murase, S.K. Nayar. Learning and recognition of 3-D objects from brightness images. In Proc. AAAI Fall Symp.: Machine Learning in Computer Vision, 25–29, AAAI Press, 1993.

    Google Scholar 

  15. A.R. Pope, D.G. Lowe. Learning object recognition models from images. In Proc. Int. Conf. Computer Vision, 296–301, 1993.

    Google Scholar 

  16. A.R. Pope. Learning to Recognize Objects in Images: Acquiring and Using Probabilistic Models of Appearance. Ph.D. thesis, Univ. of British Columbia, 1995. WWW http://www.cs.ubc.ca/spider/pope/Thesis.html.

    Google Scholar 

  17. I. Rigoutsos, R. Hummel. Distributed Bayesian object recognition. In Proc. Conf. Computer Vision and Patt. Recognit., 180–186, 1993.

    Google Scholar 

  18. J. Rissanen. A universal prior for integers and estimation by minimum description length. Annals of Statistics 11:416–431, 1983.

    Google Scholar 

  19. K.B. Sarachik, W.E.L. Grimson. Gaussian error models for object recognition. In Proc. Conf. Computer Vision and Patt. Recognit., 400–406, 1993.

    Google Scholar 

  20. M. Seibert, A.M. Waxman. Adaptive 3-D object recognition from multiple views. IEEE Trans. Patt. Anal. Mach. Intell. 14:107–124, 1992.

    Google Scholar 

  21. M.A. Turk, A.P. Pentland. Face recognition using eigenfaces. In Proc. Conf. Computer Vision and Patt. Recognit., 586–591, 1991.

    Google Scholar 

  22. S. van Huffel, J. Vandewalle. The Total Least Squares Problem: Computational Aspects and Analysis. SIAM, 1991.

    Google Scholar 

  23. W.M. Wells III. Statistical Object Recognition. Ph.D. thesis, MIT, 1992.

    Google Scholar 

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Author information

Authors and Affiliations

  1. David Sarnoff Research Center, CN 5300, 08543-5300, Princeton, NJ

    Arthur R. Pope

  2. Dept. of Computer Science, University of British Columbia, V6T 1Z4, Vancouver, B.C., Canada

    David G. Lowe

Authors
  1. Arthur R. Pope
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  2. David G. Lowe
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Editor information

Jean Ponce Andrew Zisserman Martial Hebert

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© 1996 Springer-Verlag Berlin Heidelberg

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Pope, A.R., Lowe, D.G. (1996). Learning appearance models for object recognition. In: Ponce, J., Zisserman, A., Hebert, M. (eds) Object Representation in Computer Vision II. ORCV 1996. Lecture Notes in Computer Science, vol 1144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61750-7_30

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  • DOI: https://doi.org/10.1007/3-540-61750-7_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61750-1

  • Online ISBN: 978-3-540-70673-1

  • eBook Packages: Springer Book Archive

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