Full Description
This book proposes a new approach to handle the problem of limited training data. Common approaches to cope with this problem are to model the shape variability independently across predefined segments or to allow artificial shape variations that cannot be explained through the training data, both of which have their drawbacks. The approach presented uses a local shape prior in each element of the underlying data domain and couples all local shape priors via smoothness constraints. The book provides a sound mathematical foundation in order to embed this new shape prior formulation into the well-known variational image segmentation framework. The new segmentation approach so obtained allows accurate reconstruction of even complex object classes with only a few training shapes at hand.
Contents
Basics.- Statistical Shape Models (SSMs).- A Locally Deformable Statistical Shape Model (LDSSM).- Evaluation of the Locally Deformable Statistical Shape Model.- Global-To-Local Shape Priors for Variational Level Set Methods.- Evaluation of the Global-To-Local Variational Formulation.- Conclusion and Outlook.
