Sampling image segmentations for uncertainty quantification

Sampling image segmentations for uncertainty quantification

In this paper, we introduce a method to automatically produce plausible image segmentation samples from a single expert segmentation. A probability distribution of image segmentation boundaries is defined as a Gaussian process, which leads to segmentations which are spatially coherent and consistent with the presence of salient borders in the image. The proposed approach is computationally effi...

A Comparison of Sampling Techniques for Uncertainty Quantification

Uncertainty quantification in numerical simulations generally relies on applying sampling techniques to select input parameters for a suite of deterministic calculations and then combining the results of the calculations into output distributions. Several approaches, including metamodeling and Latin Hypercube Sampling, are prevalent today. This paper will focus on the comparison of several comm...

Deterministic Sampling for Quantification of Modeling Uncertainty of Signals

Adaptive Selection of Sampling Points for Uncertainty Quantification

We present a simple and robust strategy for the selection of sampling points in Uncertainty Quantification. The goal is to achieve the fastest possible convergence in the cumulative distribution function of a stochastic output of interest. We assume that the output of interest is the outcome of a computationally expensive nonlinear mapping of an input random variable, whose probability density ...

MCMC-Based Image Reconstruction with Uncertainty Quantification

The connection between Bayesian statistics and the technique of regularization for inverse problems has been given significant attention in recent years. For example, Bayes’ Law is frequently used as motivation for variational regularization methods of Tikhonov type. In this setting, the regularization function corresponds to the negative-log of the prior probability density; the fit-to-data fu...