Markov Chain Monte Carlo shape sampling using level sets

Siqi Chen, Richard J. Radke
2009 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops  
In this paper, we show how the Metropolis-Hastings algorithm can be used to sample shapes from a distribution defined over the space of signed distance functions. We extend the basic random walk Metropolis-Hastings method to high-dimensional curves using a proposal distribution that can simultaneously maintain the signed distance function property and the ergodic requirement. We show that detailed balance is approximately satisfied and that the Markov chain will asymptotically converge. A key
more » ... y converge. A key advantage of our approach is that the shape representation is implicit throughout the process, as compared to existing work where explicit curve parameterization is required. Furthermore, our framework can be carried over to 3D situations easily. We show several applications of the framework to shape sampling from multimodal distributions and medical image segmentation.
doi:10.1109/iccvw.2009.5457687 dblp:conf/iccvw/ChenR09 fatcat:6aui5vwdnvayrap337yo7tsesa