Markov random field modeling in computer vision
Markov random field modeling in computer vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Order-Preserving Moves for Graph-Cut-Based Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized ordering constraints for multilabel optimization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We consider labeling an image with multiple tiers. Tiers, one on top of another, enforce a strict vertical order among objects (e.g. sky is above the ground). Two new ideas are explored: First, under a simplification of the general tiered labeling framework proposed by Felzenszwalb and Veksler [1], we design an efficient O(KN) algorithm for the approximate optimal labeling of an image of N pixels with K tiers. Our algorithm runs in over 100 frames per second on images of VGA resolutions when K is less than 6. When K=3, our solution overlaps with the globally optimal one by Felzenszwalb and Veksler in over 99% of all pixels but runs 1000 times faster. Second, we define a topological prior that specifies the number of local extrema in the tier boundaries, and give an O(NM) algorithm to find a single, optimal tier boundary with exactly M local maxima and minima. These two extensions enrich the general tiered labeling framework and enable fast computation. The proposed topological prior further improves the accuracy in labeling details.