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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.

By: Contributor(s): Material type: TextTextSeries: Studies in Systems, Decision and Control ; 98Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Description: XXI, 259 p. 84 illus., 64 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319535081
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
Online resources:
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.
In: Springer eBooksSummary: 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.
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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.

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.