Skip to main content

Recognizing Objects by Their Appearance Using Eigenimages

  • Conference paper
  • First Online:
SOFSEM 2000: Theory and Practice of Informatics (SOFSEM 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1963))

Abstract

The appearance-based approaches to vision problems have recently received a renewed attention in the vision community due to their ability to deal with combined effects of shape, reflectance properties, pose in the scene, and illumination conditions. Besides, appearancebased representations can be acquired through an automatic learning phase which is not the case with traditional shape representations. The approach has led to a variety of successful applications, e. g., visual positioning and tracking of robot manipulators, visual inspection, and human face recognition.

In this paper we will review the basic methods for appearance-based object recognition. We will also identify the major limitations of the standard approach and present algorithms how these limitations can be alleviated leading to an object recognition system which is applicable in real world situations.

H. B. was supported by a grant from the Austrian National Fonds zur Förderung der wissenschaftlichen Forschung (P13981INF) and the K plus Competence Center ADVANCED COMPUTER VISION. A. L. acknowledges the support from the Ministry of Science and Technology of Republic of Slovenia (Project J2-0414).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. T. W. Anderson. An Introduction to Multivariate Statistical Analysis. New York: Wiley, 1958. 248

    MATH  Google Scholar 

  2. A. Bab-Hadiashar and D. Suter. Optic flow calculation using robust statistics. In Proc. CVPR’97, pages 988–993, 1997. 255

    Google Scholar 

  3. P. Baldi and K. Hornik. Learning in linear neural networks: A survey. IEEE Transactions on Neural Networks, 6(4):837–858, 1995. 250

    Article  Google Scholar 

  4. P. Belhumeuer, J. Hespanha, and D. Kriegman. Eigenfaces vs. fischerfaces: Recognition using class specific projection. In Proc. ECCV, pages 45–58. Springer, 1996. 262

    Google Scholar 

  5. D. Beymer and T. Poggio. Face recognition from one example view. In Proceedings of 5th ICCV’95, pages 500–507. IEEE Computer Society Press, 1995. 246

    Google Scholar 

  6. I. Biederman. Recognition by components: A theory of human image understanding. Psychological Review, 94:115–147, 1987. 246

    Article  Google Scholar 

  7. H. Bischof and A. Leonardis. Recovery of eigenimages from responses of local filter banks. In R. Sablatnig, editor, Applications of 3D-Imaging and Graph-based Modelling 2000, volume 142 of OCG Schriftenreihe, pages 121–128. Österreichische Computer Gesellschaft, 2000. 259, 263

    Google Scholar 

  8. Horst Bischof and Aleš Leonardis. Robust recognition of scaled eigenimages through a hierarchical approach. In Proc. of CVPR98, pages 664–670. IEEE Compter Society Press, 1998. 259, 261

    Google Scholar 

  9. M. Black and A. Jepson. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision, 26(1):63–84, 1998. 253

    Article  Google Scholar 

  10. H. Bourlard and Y. Kamp. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics, 59:291–294, 1988. 251

    Article  MATH  MathSciNet  Google Scholar 

  11. H. H. Bülthosf, S. Y. Edelman, and M. Tarr. How are three-dimensional objects represented in the brain? Technical Report A. I. Memo No. 1479, C. B. C. L. Paper No. 9, Massachusetts Institute of Technology, 1994. 263

    Google Scholar 

  12. J. Edwards and H. Murase. Appearance matching of occluded objects using coarseto-fine adaptive masks. In Proc. CVPR’97, pages 533–539, 1997. 253

    Google Scholar 

  13. H. Farid and E. H. Adelson. Separating reflections and lighting using independent components analysis. In CVPR99, pages I:262–267, 1999. 263

    Google Scholar 

  14. M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications ACM, 24(6):381–395, June 1981. 254, 255

    Article  MathSciNet  Google Scholar 

  15. B. J. Frey, A. Colmenarez, and T. S. Huang. Mixtures of local linear subspaces for face recognition. In Proc. CVPR98, pages 32–37. IEEE Computer Society, 1998. 262

    Google Scholar 

  16. P. M. Hall, D. Marshall, and R.R. Martin. Incremental eigenanalysis for classification. TR 98001, Dept. of Computer Science, Univ. of Cardi., 1998. 251

    Google Scholar 

  17. Geoffrey E Hinton, Michael Revow, and Peter Dayan. Recognizing handwritten digits using mixtures of linear models. In G. Tesauro, D. Touretzky, and T. Leen, eds, NIPS, volume 7, pages 1015–1022. The MIT Press, 1995. 262

    Google Scholar 

  18. A. Hyvärinen and E. Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4–5):411–431, 2000. 263

    Article  Google Scholar 

  19. Ales Leonardis and Horst Bischof. Multiple Eigenspaces by MDL (in press). In Proceedings of ICPR2000. IEEE Computer Society, 2000. 262

    Google Scholar 

  20. Aleš Leonardis and Horst Bischof. Robust recognition using eigenimages. Computer Vision and Image Understanding, 78(1):99–118, 2000. 253, 255, 256, 257

    Article  Google Scholar 

  21. David Marr. Vision. New York: Freeman, 1982. 246

    Google Scholar 

  22. B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. PAMI, 19(7):696, 1997. 250

    Google Scholar 

  23. H. Murase and S. K. Nayar. Image spotting of 3D objects using parametric eigenspace representation. In G. Borgefors, editor, The 9th Scandinavian Conference on Image Analysis, volume 1, pages 323–332, Uppsala, Sweden, June 1995. 246, 253

    Google Scholar 

  24. H. Murase and S. K. Nayar. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 14:5–24, 1995. 246, 250, 251

    Article  Google Scholar 

  25. H. Murase and S.K. Nayar. Illumination planning for object recognition using parametric eigenspaces. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(12):1219–1227, 1994. 246, 248

    Article  Google Scholar 

  26. S. K. Nayar, H. Murase, and S. A. Nene. Learning, positioning, and tracking visual appearance. In IEEE International Conference on Robotics and Automation, San Diego, May 1994. 246

    Google Scholar 

  27. S. A. Nene, S. K. Nayar, and H. Murase. Columbia object image library (COIL-20). Technical Report CUCS-005-96, Columbia University, New York, 1996. 249, 257

    Google Scholar 

  28. K. Ohba and K. Ikeuchi. Detectability, uniqueness, and reliability of eigen windows for stable verification of partially occluded objects. PAMI, 9:1043–1047, 1997. 253

    Google Scholar 

  29. A. Pentland, B. Moghaddam, and T. Straner. View-based and modular eigenspaces for face recognition. Technical Report 245, MIT Media Laboratory, 1994. 253

    Google Scholar 

  30. T. Poggio and S. Edelman. A network that learns to recognize three-dimensional objects. Nature, 343:263–266, 1990. 248, 263

    Article  Google Scholar 

  31. R. Rao. Dynamic appearance-based recognition. In CVPR’97, pages 540–546. IEEE Computer Society, 1997. 253

    Google Scholar 

  32. J. Rissanen. Stochastic Complexity in Statistical Inquiry, volume 15 of Series in Computer Science. World Scientific, 1989. 256

    Google Scholar 

  33. P. J. Rousseuw and A. M. Leroy. Robust Regression and Outlier Detection. Wiley, New York, 1987. 254

    Book  Google Scholar 

  34. S. Chandrasekaran, B. S. Manjunath, Y. F. Wang, J. Winkler, and H. Zhang. An eigenspace update algorithm for image analysis. Technical Report TR CS 96-04, Dept. of Computer Science, Univ. of California, Santa Barbara, 1996. 251

    Google Scholar 

  35. B. Schölkopf, A. Smola, and K. R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299–1319, 1998. 262

    Article  Google Scholar 

  36. E. Simoncelli and H. Farid. Steerable wedge filters for local orientation analysis. IEEE Trans. on Image Processing, pages 1–15, 1996. 261

    Google Scholar 

  37. M. Stricker and A. Leonardis. ExSel++: A general framework to extract parametric models. In V. Hlavac and R. Sara, editors, 6th CAIP’95, number 970 in Lecture Notes in Computer Science, pages 90–97, Prague, Czech Republic, September 1995. Springer. 262

    Google Scholar 

  38. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991. 246, 248, 250

    Article  Google Scholar 

  39. Shimon Ullman. High-level Vision. MIT Press, 1996. 248

    Google Scholar 

  40. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995. 262

    Google Scholar 

  41. S. Yoshimura and T. Kanade. Fast template matching based on the normalized correlation by using multiresolution eigenimages. In Proceedings of IROS’94, pages 2086–2093, 1994. 246, 248

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bischof, H., Leonardis, A. (2000). Recognizing Objects by Their Appearance Using Eigenimages. In: Hlaváč, V., Jeffery, K.G., Wiedermann, J. (eds) SOFSEM 2000: Theory and Practice of Informatics. SOFSEM 2000. Lecture Notes in Computer Science, vol 1963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44411-4_15

Download citation

  • DOI: https://doi.org/10.1007/3-540-44411-4_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41348-6

  • Online ISBN: 978-3-540-44411-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics