Detail of the student project

Topic:Markov chain Monte Carlo segmentation for fitting geometrical model
Department:Katedra kybernetiky
Supervisor:prof. Dr. Ing. Jan Kybic
Announce as:Diplomová práce, Bakalářská práce, Semestrální projekt
Description:Some objects have internal structure - they contain subobjects with a particular geometric relationship. To segment such objects, Markov chain Monte Carlo (MCMC) methods can be used to generate data-driven hypotheses with statistical shape priors. We shall explore methods of this type, first on synthetic data and then to segment real structured objects such as Drosophila eggs, which are known to contain 15 so-called nurse cells. A particular attention will be paid to MCMC efficiency.
Bibliography:Erdil et al: MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors. CVPR 2016
Responsible person: Petr Pošík