By Topic

Nonstationary AR modeling and constrained recursive estimation of the displacement field

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Efstratiadis, S.N. ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; Katsaggelos, A.K.

An approach to constrained recursive estimation of the displacement vector field (DVF) in image sequences is presented. An estimate of the displacement vector at the working point is obtained by minimizing the linearized displaced frame difference based on a set of observations that belong to a causal neighborhood (mask). An expression for the variance of the linearization error (noise) is obtained. Because the estimation of the DVF is an ill-posed problem, the solution is constrained by considering an autoregressive (AR) model for the DVF. A nonstationary AR model of the DVF is also considered. Additional information about the solution is incorporated into the algorithm using a causal oriented smoothness constraint. A set theoretic regularization approach based on this formulation results in a weighted constrained least-squares estimation of the DVF. The algorithm shows an improved performance with respect to accuracy, robustness of occlusion, and smoothness of the estimated DVF when applied to typical videoconferencing scenes

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:2 ,  Issue: 4 )