Title
Recovering Occlusion Boundaries from an Image
Abstract
Occlusion reasoning is a fundamental problem in computer vision. In this paper, we propose an algorithm to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Rather than viewing the problem as one of pure image processing, our approach employs cues from an estimated surface layout and applies Gestalt grouping principles using a conditional random field (CRF) model. We propose a hierarchical segmentation process, based on agglomerative merging, that re-estimates boundary strength as the segmentation progresses. Our experiments on the Geometric Context dataset validate our choices for features, our iterative refinement of classifiers, and our CRF model. In experiments on the Berkeley Segmentation Dataset, PASCAL VOC 2008, and LabelMe, we also show that the trained algorithm generalizes to other datasets and can be used as an object boundary predictor with figure/ground labels.
Year
DOI
Venue
2011
10.1007/s11263-010-0400-4
International Journal of Computer Vision
Keywords
Field
DocType
Image segmentation,Occlusion boundaries,Figure/ground labeling,Image interpretation,Scene understanding,3D reconstruction,Depth from image,Edge detection
Iterative refinement,Scale-space segmentation,Computer science,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Conditional random field,Computer vision,LabelMe,Pattern recognition,Segmentation,Machine learning
Journal
Volume
Issue
ISSN
91
3
0920-5691
Citations 
PageRank 
References 
84
5.94
60
Authors
3
Name
Order
Citations
PageRank
Derek Hoiem14998302.66
Alexei A. Efros210301634.66
Martial Hebert3112771146.89