Name
Abstract | ||
|---|---|---|
We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm. We define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution. We demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-75759-7_58 | MICCAI (2) |
Keywords | Field | DocType |
markov chain monte carlo,image segmentation,probability distribution,local minima,sampling methods,transition probability | Rejection sampling,Markov chain Monte Carlo,Metropolis–Hastings algorithm,Computer science,Posterior probability,Probability distribution,Artificial intelligence,Gibbs sampling,Slice sampling,Mathematical optimization,Pattern recognition,Hybrid Monte Carlo,Algorithm | Conference |
Volume | Issue | ISSN |
10 | Pt 2 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-75758-9 | 19 | 1.16 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayres Fan | 1 | 491 | 25.93 |
| John W. Fisher III | 2 | 878 | 74.44 |
| William M. Wells III | 3 | 5267 | 833.10 |
| James J. Levitt | 4 | 129 | 10.31 |
| Alan S. Willsky | 5 | 7466 | 847.01 |