Structural motion segmentation based on probabilistic clustering

Cha-Keon Cheong, Kiyoharu Aizawa

Abstract: In order to extract a meaningful scene structure from an image sequence, the global and local motion of moving objects are taken into consideration. Firstly, the image sequences are roughly separated into the regions of moving objects based on probabilistic clustering with mixture models using optical flow and the image intensity. For each moving object cluster, parametric motion estimation and segmentation can be obtained by iterative estimation of the affine motion parameters and region modification according to a criterion using the Gauss-Newton iterative optimization algorithm.