Motion Segmentation in Long Image Sequences
Steven J. Mills, Kevin L. Novins · 2000
Long image sequences provide a wealth of information, which means that a compact representation is needed to efficiently process them. In this paper a novel representation for motion segmentation in long image sequences is presented. This representation – the feature interval graph – measures the pairwise rigidity of features in the scene. The feature interval graph is re-cursively computed, making it a compact representation, and uses an interval model of uncertainty. The feature interval graph forms the basis for new al-gorithms for motion segmentation and occlusion analysis. Results of these algorithms are presented on synthetic and laboratory scenes. 1