Online parameter estimation in dynamic Markov Random Fields for image sequence analysis
Markov Random Fields (MRF) have proven to be extremely useful models for efficient and accurate image segmentation.Recent literature points to an increased effort towards incorporating useful priors (shape, geometry, context) in a MRF framework. However, topological priors, considered extremely crucial in biological and natural image sequences have been less explored. This work proposes a strategy wherein free parameters of the MRF are used to make it topology aware using a semantic graphical model working in conjunction with the MRF. Estimation of free parameters is constrained by prior knowledge of an object's topological dynamics encoded by the graphical model. Maximizing a regional conformance measure yields parameters for the frame under consideration. The application motivating this work is the tracing of neuronal structures across 3D serial section Transmission Electron Micrograph (ssTEM) stacks. Applicability of the proposed method is demonstrated by tracing 3D structures in ssTEM stacks.