From Global to Local Statistical Shape Priors
Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes
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Beschreibung
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.
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.
Is understandable, readable, and well-structured with numerous illustrations Presents interesting, new, and powerful concepts Serves as a “gateway drug” to the field thanks to its unique presentation style Includes supplementary material: sn.pub/extras
Autor*in
Carsten Last
Themen in »From Global to Local Statistical Shape Priors«
Global Statistical Shape Priors Pattern Recognition Image Processing Computer Vision Object Segmentation Image Segmentation