Paper Info
Title
Efficient topology-controlled sampling of implicit shapes
Abstract
Sampling from distributions of implicitly defined shapes enables analysis of various energy functionals used for image segmentation. Recent work [1] describes a computationally efficient Metropolis- Hastings method for accomplishing this task. Here, we extend that framework so that samples are accepted at every iteration of the sampler, achieving an order of magnitude speed up in convergence. Additionally, we show how to incorporate topological constraints.
Year
DOI
Venue
2012
10.1109/ICIP.2012.6466904
Image Processing
Keywords
DocType
Volume
Markov processes,Monte Carlo methods,convergence of numerical methods,image sampling,image segmentation,iterative methods,topology,MCMC method,Markov chain Monte Carlo method,Metropolis-Hastings method,convergence,energy functionals,image segmentation,implicit shapes,magnitude speed,topological constraints,topology-controlled sampling,MCMC,Markov chain Monte Carlo,Metropolis-Hastings,level sets,segmentation
Conference
abs/1205.3766
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4673-2532-5
978-1-4673-2532-5
2
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Jason Chang11336.75
John W. Fisher III287874.44