Regularized motion estimation using robust entropic functionals

Damon L. Tull*, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations


In this paper, the regularized estimation of the displacement vector field (DVF) of a dynamic image sequence is considered. A new class of non-quadratic convex regularization functionals is employed to estimate the motion field in the presence of motion discontinuities and occlusions. The derivation of the functionals is based on entropy considerations and do not require parameter tuning as in previously proposed methods. This new class of functionals is both robust and convex making it possible to preserve motion boundaries and obtain a globally optimum solution. The performance of entropic functionals is compared to previously suggested functionals for motion estimation using real and synthetic image sequences.

Original languageEnglish (US)
Number of pages4
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3) - Washington, DC, USA
Duration: Oct 23 1995Oct 26 1995


OtherProceedings of the 1995 IEEE International Conference on Image Processing. Part 3 (of 3)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


Dive into the research topics of 'Regularized motion estimation using robust entropic functionals'. Together they form a unique fingerprint.

Cite this