Parallel tempering strategies for model-based landmark detection on shapes.

ABSTRACT: In the field of shape analysis, landmarks are defined as a low-dimensional, representative set of important features of an object's shape that can be used to identify regions of interest along its outline. An important problem is to infer the number and arrangement of landmarks, given a set of shapes drawn from a population. One proposed approach defines a posterior distribution over landmark locations by associating each landmark configuration with a linear reconstruction of the shape. In practice, sampling from the resulting posterior density is challenging using standard Markov chain Monte Carlo (MCMC) methods because multiple configurations of landmarks can describe a complex shape similarly well, manifesting in a multi-modal posterior with well-separated modes. Standard MCMC methods t

SUBMITTER: Strait J 

PROVIDER: S-EPMC9216184 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature