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 New Developments in Soil Characterization and Soil Stability by Carsten Last
Cover of the book Betty A. Reardon: Key Texts in Gender and Peace by Carsten Last
Cover of the book Ad Hoc Networks by Carsten Last
Cover of the book The Dynamics of Iranian Borders by Carsten Last
Cover of the book MediaSync by Carsten Last
Cover of the book Atlas of Multiparametric Prostate MRI by Carsten Last
Cover of the book Soft Computing Techniques in Engineering Applications by Carsten Last
Cover of the book Civil Rights in America and the Caribbean, 1950s–2010s by Carsten Last
Cover of the book Human Rights-Based Approaches to Clinical Social Work by Carsten Last
Cover of the book Aerial Manipulation by Carsten Last
Cover of the book Transfer Pricing in SMEs by Carsten Last
Cover of the book Kant’s Ethics and the Same-Sex Marriage Debate - An Introduction by Carsten Last
Cover of the book Recurrent Neural Networks for Short-Term Load Forecasting by Carsten Last
Cover of the book Operations, Logistics and Supply Chain Management by Carsten Last
Cover of the book Adversary Detection For Cognitive Radio Networks 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