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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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| Lenguaje: | eng |
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Springer
2017
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| Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-53508-1 http://cds.cern.ch/record/2258649 |