Advertisement

Using Inter-feature-Line Consistencies for Sequence-Based Object Recognition

  • Jiun-Hung Chen
  • Chu-Song Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

Abstract

An image sequence-based framework for appearance-based object recognition is proposed in this paper. Compared with the methods of using a single view for object recognition, inter-frame consistencies can be exploited in a sequence-based method, so that a better recognition performance can be achieved. We use the nearest feature line method (NFL) [8] to model each object. The NFL method is extended in this paper by further integrating motion-continuity information between features lines in a probabilistic framework. The associated recognition task is formulated as maximizing an a posteriori probability measure. The recognition problem is then further transformed to a shortest-path searching problem, and a dynamic-programming technique is used to solve it.

Keywords

Source Node Face Recognition Object Recognition Sink Node Feature Line 
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.
    Belhumeur, P.N., Kriegman, D.J.: What is the Set of Images of an Object under all Possible Illumination Conditions? International Journal of Computer Vision 28, 245–260 (1998)CrossRefGoogle Scholar
  2. 2.
    Black, M.J., Jepson, A.: EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation. International Journal of Computer Vision 26, 63–84 (1998)CrossRefGoogle Scholar
  3. 3.
    Chen, L.-F., Liao, H.-Y.M., Ko, M.-T., Lin, J.-C., Yu, G.-J.: A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem. Pattern Recognition 33, 1713–1726 (2000)CrossRefGoogle Scholar
  4. 4.
    Chen, Y.-S., et al.: Video-based Eye Tracking for Autostereoscopic Displays. Optical Engineering 40, 2726–2734 (2001)CrossRefGoogle Scholar
  5. 5.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (1990)zbMATHGoogle Scholar
  6. 6.
    Hung, Y.-P., Chen, C.-S., Tsai, Y.-P., Lin, S.-W.: Augmenting Panoramas with Object Movies by Generating Novel Views with Disparity-Based View Morphing. Journal of Visualization and Computer Animation 13, 237–247 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Li, S.Z., Chan, K.L., Wang, C.L.: Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1335–1339 (2000)CrossRefGoogle Scholar
  8. 8.
    Li, S.Z., Lu, J.: Face Recognition Using the Nearest Feature Line Method. IEEE Transactions on Neural Networks 10, 439–443 (1999)CrossRefGoogle Scholar
  9. 9.
    Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 696–710 (1997)CrossRefGoogle Scholar
  10. 10.
    Murase, H., Nayar, S.K.: Visual Learning and Recognition of 3D Objects from Appearance. International Journal of Computer Vision 14, 5–24 (1995)CrossRefGoogle Scholar
  11. 11.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100), Technical Report CUCS-006-96, Columbia University (1996)Google Scholar
  12. 12.
    Ohba, K., Ikeuchi, K.: Detectability, Uniqueness, and Reliability of Eigen Widows for Stable Verification of Partially Occluded Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 1043–1048 (1997)CrossRefGoogle Scholar
  13. 13.
    Papageorgious, C., Poggio, T.: A Pattern Classification Approach to Dynamic Object Detection. In: Proceedings of International Conference on Computer Vision, Corfu, Greece, pp. 1223–1228 (1999)Google Scholar
  14. 14.
    Roobaert, D., van Hulle, M.M.: View-based 3D Object Recognition with Support Vector Machines. In: Proceedings of 1999 IEEE International Workshop on Neural Networks for Signal Processing, Madison, Wisconsin, USA, pp. 77–84 (1999)Google Scholar
  15. 15.
    Roth, D., Yang, M.-H., Ahuja, N.: Learning to Recognize 3D Objects. Neural Computation 14, 1071–1103 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Ullman, S., Basri, R.: Recognition by Linear Combinations of Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 992–1006 (1991)CrossRefGoogle Scholar
  17. 17.
    Vetter, T., Poggio, T.: Linear Object Classes and Image Synthesis From a Single Example Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 696–710 (1997)CrossRefGoogle Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, pp. 511–518 (2001)Google Scholar
  19. 19.
    Yuan, C., Niemann, H.: An Appearance Based Neural Image Processing for 3-D Object Recognition. In: Proceedings of International Conference on Image Processing, Vancouver, Canada, pp. 344–347 (2000)Google Scholar
  20. 20.
    Zhou, Z., Li, S.Z., Chan, K.L.: A Theoretical Justification of Nearest Feature Line Method. In: Proceedings of International Conference on Pattern Recognition, Barcelona, Spain, pp. 759–762 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jiun-Hung Chen
    • 1
  • Chu-Song Chen
    • 2
  1. 1.University of WashingtonSeattleUSA
  2. 2.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

Personalised recommendations