24 June 2005 Adaptive mixed norm optical flow estimation
Author Affiliations +
Proceedings Volume 5960, Visual Communications and Image Processing 2005; 59603W (2005) https://doi.org/10.1117/12.632674
Event: Visual Communications and Image Processing 2005, 2005, Beijing, China
Abstract
The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regularized approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Mixed Norm (MN) to estimate the best motion vector for a given pixel. In our model, various types of noise can be handled, representing different sources of error. The motion vector estimation takes into consideration local image properties and it results from the minimization of a mixed norm functional with a regularization parameter depending on the kurtosis. This parameter determines the relative importance of the fourth norm and makes the functional convex. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Experiments indicate that this approach provides robust estimates of the optical flow.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vania Vieira Estrela, Matthias O. Franz, Ricardo T. Lopes, A. P. De Araújo, "Adaptive mixed norm optical flow estimation", Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59603W (24 June 2005); doi: 10.1117/12.632674; https://doi.org/10.1117/12.632674
PROCEEDINGS
8 PAGES


SHARE
Back to Top