Screen LibraryFrom 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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| Main Authors: | |
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| Corporate Authors: | |
| Published: |
Springer International Publishing : Imprint: Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Studies in Systems, Decision and Control,
98 |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-319-53508-1 |
| 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 w |
| Carrier Form: | 1 online resource (XXI, 259 pages): illustrations. |
| ISBN: | 9783319535081 |
| Index Number: | Q342 |
| CLC: | TP18 |
| 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. |