|
Published Articles >> Table of Contents >> Abstract
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2
p. 908
Controlling Model Complexity in Flow Estimation
Z. Duric, George Mason University
F. Li, George Mason University
H. Wechsler, George Mason University
V. Cherkassky, University of Minnesota
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2003.1238445
Send link to a friend
| Abstract |
|
This paper describes a novel application of Statistical Learning Theory (SLT) to control model complexity in flow estimation. SLT provides analytical generalization bounds suitable for practical model selection from small and noisy data sets of image measurements (normal flow). The method addresses the aperture problem by using the penalized risk (ridge regression). We demonstrate an application of this method on both synthetic and real image sequences and use it for motion interpolation and extrapolation. Our experimental results show that our approach compares favorably against alternative model selection methods such as the Akaike's final prediction error, Schwartz's criterion, Generalized cross-validation, and Shibata's model selector.
|
 Additional Information
|
Citation:
Z. Duric, F. Li, H. Wechsler, V. Cherkassky,
"Controlling Model Complexity in Flow Estimation,"
iccv,
p. 908,
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2,
2003
|
|
