Date of Original Version
IEEE International Conference on Computer Vision (ICCV), October, 2007
Abstract or Table of Contents
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.