From Global to Local Statistical Shape Priors

Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Artificial Intelligence, General Computing
Cover of the book From Global to Local Statistical Shape Priors by Carsten Last, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Carsten Last ISBN: 9783319535081
Publisher: Springer International Publishing Publication: March 14, 2017
Imprint: Springer Language: English
Author: Carsten Last
ISBN: 9783319535081
Publisher: Springer International Publishing
Publication: March 14, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Future Access Enablers for Ubiquitous and Intelligent Infrastructures by Carsten Last
Cover of the book Wireless Virtualization by Carsten Last
Cover of the book Silica Stories by Carsten Last
Cover of the book Democracy and Judicial Reforms in South-East Europe by Carsten Last
Cover of the book Adrenal Disorders by Carsten Last
Cover of the book Conducting Polymer Hybrids by Carsten Last
Cover of the book Languages, Design Methods, and Tools for Electronic System Design by Carsten Last
Cover of the book Designing a Place Called Home by Carsten Last
Cover of the book Quantum Mechanics for Pedestrians 2: Applications and Extensions by Carsten Last
Cover of the book Advances in Affective and Pleasurable Design by Carsten Last
Cover of the book Emerging Issues in Global Marketing by Carsten Last
Cover of the book Dependable Software Engineering. Theories, Tools, and Applications by Carsten Last
Cover of the book Psychology of Retention by Carsten Last
Cover of the book Disentangling Participation by Carsten Last
Cover of the book Technology Enhanced Learning by Carsten Last
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy