Perceptual completion of occluded surfaces
Computer Vision and Image Understanding
Perceptual organization of occluding contours of opaque surfaces
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Machine Learning
Segmentation with Depth but Without Detecting Junctions
Journal of Mathematical Imaging and Vision
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Markov Random Field Model for Figure-Ground Segregation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
MosaicShape: Stochastic Region Grouping with Shape Prior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An easy measure of compactness for 2D and 3D shapes
Pattern Recognition
A Unified Curvature Definition for Regular, Polygonal, and Digital Planar Curves
International Journal of Computer Vision
International Journal of Computer Vision
Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning
International Journal of Computer Vision
Exploiting T-junctions for depth segregation in single images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Journal of Artificial Intelligence Research
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Accurate semantic image labeling by fast geodesic propagation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
Representing moving images with layers
IEEE Transactions on Image Processing
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To bring computer vision closer to human vision, we attempt to enable computer to understand the occlusion relationship in an image. In this paper, we propose five low dimensional region-based occlusion cues inspired by the human perception of occlusion. These cues are semantic cue, position cue, compactness cue, shared boundary cue and junction cue. We apply these cues to predict the region-wise occlusion relationship in an image and infer the layer sequence of the image scene. A preference function, trained with samples consisting of these cues, is defined to predict the occlusion relationship in an image. Then we put all the occlusion predictions into the layering algorithm to infer the layer sequence of the image scene. The experiments on rural, artificial and outdoor scene datasets show the effectiveness of our method for occlusion relationship prediction and image scene layering.