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

• Κύριος συγγραφέας: Last, Carsten (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Studies in Systems, Decision and Control, 98
• Θέματα: Engineering.; Artificial intelligence.; Computer graphics.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Computer Imaging, Vision, Pattern Recognition and Graphics.

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...

Πίνακας περιεχομένων:

• 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.