By Topic

Information integration for accurate foreground segmentation in complex scenes

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 $31
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

4 Author(s)
Shen, J. ; Dept. of Electron. Eng., Tsinghua Univ., Beijing, China ; Yang, W. ; Lu, Z. ; Liao, Q.

The authors propose a hybrid framework that combines frame difference and background subtraction to integrate complementary sources of information for monocular video segmentation. This framework is modelled as an optimisation process of an energy function, which is established on a Markov random field (MRF) and optimised by Gibbs sampling. It provides a way to exploit different kinds of information obtained from frame difference and background subtraction. Central to the proposed method are two facts - that shape prior can be flexibly obtained from frame difference, and shadow removal can be integrated into the framework with a background texture model. The experiments show that this approach reliably and accurately performs on sequences that include different scenarios (indoors, outdoors) and also addresses several canonical segmentation problems, such as camouflage, foreground aperture and so forth.

Published in:

Image Processing, IET  (Volume:6 ,  Issue: 5 )