Actions for From Global to Local Statistical Shape Priors [electronic resource] : Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes

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From Global to Local Statistical Shape Priors [electronic resource] : Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes / by Carsten Last

Author
Last, Carsten
Published
Cham : Springer International Publishing : Imprint: Springer, 2017.
Physical Description
XXI, 259 pages 84 illustrations, 64 illustrations in color : online resource
Additional Creators
SpringerLink (Online service)
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Series
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.
Summary
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.
Subject(s)
ISBN
9783319535081
Digital File Characteristics
text file PDF
Part Of
Springer eBooks
View MARC record | catkey: 20046900