Covariance Estimation for SAD Block Matching

  • Johan Skoglund
  • Michael Felsberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

The estimation of a patch position in an image is a long established but still relevant topic with many applications, e.g. pose estimation and tracking in image sequences. In most systems the position estimate needs to be fused with other estimates, and hence, covariance information is required to weight the different estimates in the right way. In this paper we address the issue with covariance estimation in the case of sum of absolute difference (SAD) block matching. First, we derive the theory for covariance estimation in the case of SAD matching. Second, we evaluate the suggested method in a virtual 3D patch tracking scenario in order to verify the performance in real-world scenarios.

Keywords

Error Function Tracking Algorithm Suggested Method Covariance Estimation Dual Frame 
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.
    Skoglund, J., Felsberg, M.: Evaluation of Subpixel Tracking Algorithms. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 374–382. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Dougherty, E.R.: Random Processes for Image and Signal Processing. SPIE press (1999)Google Scholar
  3. 3.
    Ljung, L.: Sytem Identifiaction. Prentice-Hall, Englewood Cliffs (1999)Google Scholar
  4. 4.
    Kanazawa, Y., Kanatani, K.: Do we really have to consider covariance matrices for image features? In: ICCV, vol. 2, p. 301 (2001)Google Scholar
  5. 5.
    Fessler, J.A.: Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography. IEEE Tr. Im. Proc. 5(3), 493–506 (1996)CrossRefGoogle Scholar
  6. 6.
    Sun, Q., DeJong, G.: Feature kernel functions: Improving SVMs using high-level knowledge. In: CVPR vol. 2, pp. 177–183 (2005)Google Scholar
  7. 7.
    Granlund, G., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Johan Skoglund
    • 1
  • Michael Felsberg
    • 1
  1. 1.Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, SE-581 83 LinköpingSweden

Personalised recommendations