A unified Markov random field/marked point process image model and its application to computational materials

Abstract

Markov random field (MRF) models have become extremely popular for regularization in image segmentation. They are useful for imposing local constraints, but they have limited capability for imposing global constraints. Marked Point Process (MPP) models incorporate global information, such as shape, as a prior, but local constraints, such as pixel-wise interaction, are not easily modeled. In this paper, we propose a combined MRF and MPP model and demonstrate its usefulness for micrograph analysis. Our hybrid model imposes both local and global constraints, at the pixel level as well as the object level. A multiple birth and death algorithm is then used to approximate the MAP segmentation using the hybrid model. We present experimental results to show the advantage of our model over the MPP for object identification and the MRF for segmentation.