Event Location: NSH 1507

Abstract: While great strides have been made in detecting and localizing specific
objects in natural images, the bottom-up segmentation of unknown, generic
objects remains a difficult challenge. We believe that occlusion can
provide a strong cue for object segmentation and “pop-out”, but detecting
an object’s occlusion boundaries using appearance alone is a difficult
problem in itself. If the camera or the scene is moving, however, that
motion provides an additional powerful indicator of occlusion. Thus, we
use standard appearance cues (e.g. brightness/color gradient) in addition
to motion cues that capture subtle differences in the relative surface
motion (i.e. parallax) on either side of an occlusion boundary. We
describe a learned local classifier and global inference approach which
provide a framework for combining and reasoning about these appearance and
motion cues to estimate which region boundaries of an initial over-
segmentation correspond to object/occlusion boundaries in the scene.
Through results on a dataset which contains short videos with labeled
boundaries, we demonstrate the effectiveness of motion cues for this task.