Advertisement

Resolution-Aware Fitting of Active Appearance Models to Low Resolution Images

  • Göksel Dedeoǧlu
  • Simon Baker
  • Takeo Kanade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

Active Appearance Models (AAM) are compact representations of the shape and appearance of objects. Fitting AAMs to images is a difficult, non-linear optimization task. Traditional approaches minimize the L2 norm error between the model instance and the input image warped onto the model coordinate frame. While this works well for high resolution data, the fitting accuracy degrades quickly at lower resolutions. In this paper, we show that a careful design of the fitting criterion can overcome many of the low resolution challenges. In our resolution-aware formulation (RAF), we explicitly account for the finite size sensing elements of digital cameras, and simultaneously model the processes of object appearance variation, geometric deformation, and image formation. As such, our Gauss-Newton gradient descent algorithm not only synthesizes model instances as a function of estimated parameters, but also simulates the formation of low resolution images in a digital camera. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolution and model complexity levels. Experimental results show that RAF considerably improves the estimation accuracy of both shape and appearance parameters when fitting to low resolution data.

Keywords

Root Mean Square Active Appearance Model Tracking Experiment Face Tracking Geometric Deformation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barbe, D.F.: Charge-Coupled Devices. Springer, Heidelberg (1980)CrossRefGoogle Scholar
  2. 2.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. of the 7th Int. Joint Conference on Artificial Intelligence, April 1981, pp. 674–679 (1981)Google Scholar
  3. 3.
    Anandan, P.: A Computational Framework and an Algorithm for the Measurement of Visual Motion. International Journal of Computer Vision 2(3), 283–310 (1989)CrossRefGoogle Scholar
  4. 4.
    Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical Model-Based Motion Estimation. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 237–252. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  5. 5.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Edwards, G.J., Taylor, C.J., Cootes, T.F.: Interpreting Face Images Using Active Appearance Models. In: Proc. of Int. Conf. on Automatic Face and Gesture Recognition, June 1998, pp. 300–305 (1998)Google Scholar
  7. 7.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar
  8. 8.
    Baker, S., Gross, R., Matthews, I.: Lucas-Kanade 20 Years On: A Unifying Framework: Part 3. Robotics Institute Technical Report CMU-RI-TR-03-35, Carnegie Mellon University (November 2003)Google Scholar
  9. 9.
    Baker, S., Matthews, I.: Lucas-Kanade 20 Years On: A Unifying Framework. Int. Journal of Computer Vision 56(3), 221–255 (2004)CrossRefGoogle Scholar
  10. 10.
    Matthews, I., Baker, S.: Active Appearance Models Revisited. International Journal of Computer Vision 60(2), 135–164 (2004)CrossRefGoogle Scholar
  11. 11.
    Gross, R., Matthews, I., Baker, S.: Generic vs. Person Specific Active Appearance Models. Image and Vision Computing 23(11), 1080–1093 (2005)CrossRefGoogle Scholar
  12. 12.
    Dedeoglu, G., Kanade, T., Baker, S.: The Asymmetry of Image Registration and its Application to Face Tracking. Robotics Institute Technical Report CMU-RI-TR-06-06, Carnegie Mellon University (February 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Göksel Dedeoǧlu
    • 1
  • Simon Baker
    • 1
  • Takeo Kanade
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA

Personalised recommendations