Clustering algorithms by minimizing an objective function share a clear drawback of having
to set the number of clusters manually. Although density peak clustering is able to find the …

Hidden Markov random field (HMRF) models have been widely used for image
segmentation, as they appear naturally in problems where a spatially constrained clustering …

In this paper, a new mixture model for image segmentation is presented. We propose a new
way to incorporate spatial information between neighboring pixels into the Gaussian mixture …

Stochastic modeling methods and uncertainty quantification are important tools for gaining
insight into the geological variability of subsurface structures. Previous attempts at geologic …

Integrating borehole and piezocone penetration test (CPTU) data in site characterization
helps to achieve a more comprehensive understanding of ground conditions. However, soil …

Finite mixture model based on the Student's-t distribution, which is heavily tailed and more
robust than Gaussian, has recently received great attention for image segmentation. A new …

In this paper, we present a novel spatially constrained generative model and an expectation-
maximization (EM) algorithm for model-based image segmentation. The generative model …

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain
regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni …

This paper addresses the problem of estimating the Potts parameter β jointly with the
unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) …

This paper presents a novel perspective to understanding the spatial and statistical patterns
of a cone penetration dataset and identifying soil stratification using these patterns. Both …