Abstract
To address multiple motions and deformable objects'
motions encountered in existing region-based approaches, an
automatic video object (VO) segmentation methodology is proposed
in this paper by exploiting the duality of image segmentation and
motion estimation such that spatial and temporal information
could assist each other to jointly yield much improved
segmentation results. The key novelties of our method are (1)
scale-adaptive tensor computation, (2)
spatial-constrained motion mask generation
without invoking dense motion-field computation, (3)
rigidity analysis, (4) motion mask generation and
selection, and (5) motion-constrained spatial region
merging. Experimental results demonstrate that these novelties
jointly contribute much more accurate VO segmentation both in
spatial and temporal domains.