Controlling model complexity in flow estimation

Z. Duric, F. Li, H. Wechsler, V. Cherkassky

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper describes a novel application of Statistical Learning Theory (SLT) to control model complexity inflow estimation. SLT provides analytical generalization bounds suitable for practical model selection from small and noisy data sets of image measurements (normalflow). 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 experi- mental results show that our approach compares favorably against alternative model selection methods such as the Akaike 's final prediction error, Schwartz's criterion, Gen- eralized cross-validation, and Shibata 's model selector.

Original languageEnglish (US)
Pages (from-to)908-914
Number of pages7
JournalProceedings of the IEEE International Conference on Computer Vision
Volume2
DOIs
StatePublished - 2003
EventNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION - Nice, France
Duration: Oct 13 2003Oct 16 2003

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