Jiahua Chen, Abbas Khalili Department of Statistics and Actuarial Science University of Waterloo Abstract A fundamental and challenging problem in the application of finite mixture models is to make inference on the order of the model. In this paper, we develop a new penalized likelihood approach to the order selection problem. The new method deviates from the information-based methods such as ...

In this work, we propose a new Bayesian model for unsupervised image segmentation based on combination of the spatially varying finite mixture models (SVFMMs) and non-local means (NLM) framework. The probabilistic NLM weighting function is successfully integrated into Gauss–Markov random field, yielding prior density that adaptively imposes local regularization to simultaneously preserve edges ...