Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning

Abstract

The boundaries of objects in an image are often considered a nuisance to be “handled” due to the occlusion they exhibit. Since most, if not all, computer vision techniques aggregate information spatially within a scene, information spanning these boundaries, and therefore from different physical surfaces, is invariably and erroneously considered together. In addition, these boundaries convey important perceptual information about 3D scene structure and shape. Consequently, their identification can benefit many different computer vision pursuits, from low-level processing techniques to high-level reasoning tasks. While much focus in computer vision is placed on the processing of individual, static images, many applications actually offer video, or sequences of images, as input. The extra temporal dimension of the data allows the motion of the camera or the scene to be used in processing. In this paper, we focus on the exploitation of subtle relative-motion cues present at occlusion boundaries. When combined with more standard appearance information, we demonstrate these cues’ utility in detecting occlusion boundaries locally. We also present a novel, mid-level model for reasoning more globally about object boundaries and propagating such local information to extract improved, extended boundaries.