Joint space-time image sequence segmentation based on volume competition and level sets

In this paper, we address the issue of joint space-time segmentation of image sequences. Typical approaches to such segmentation consider two image frames at a time, and perform tracking of individual segmentations across time. We propose to perform this segmentation jointly over multiple frames. This leads to a 3-D segmentation, i.e., search for a volume "carved out" by a moving object in the (3-D) image sequence domain. We pose the problem in Bayesian framework and use the MAP criterion. Under suitable structural and segmentation/motion models we convert MAP estimation to a functional minimization. The resulting problem can be viewed as volume competition, a 3-D generalization of region competition. We parameterize the unknown surface to be estimated, but rather than solving for it using an active-surface approach, we embed it into a higher-dimensional function and use the level-set methodology. We show experimental results for the simpler case of object motion against sti...

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Mirko Ristivojevic, Janusz Konrad
• Keywords: 3-D Generalization, 3-D Segmentation, ICIP 2002, Image Processing, Joint Space-time Segmentation