From Global to Local Statistical Shape Priors Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes /

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 o...

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Bibliographic Details
Main Author: Last, Carsten (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : 2017.
Imprint: Springer,
Edition:1st ed. 2017.
Series:Studies in Systems, Decision and Control, 98
Subjects:
Computational intelligence.
Artificial intelligence.
Image processing > Digital techniques.
Computer vision.
Computational Intelligence.
Artificial Intelligence.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Online Access:Full-text access
Table of 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.

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