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 Advances in Italian Mechanism Science by Carsten Last
Cover of the book The Psychology of Love and Hate in Intimate Relationships by Carsten Last
Cover of the book Hayek: A Collaborative Biography by Carsten Last
Cover of the book Worlds Beyond Our Own by Carsten Last
Cover of the book Rape Culture, Gender Violence, and Religion by Carsten Last
Cover of the book Betty A. Reardon: Key Texts in Gender and Peace by Carsten Last
Cover of the book Hypertension and the Brain as an End-Organ Target by Carsten Last
Cover of the book Introduction to Artificial Intelligence by Carsten Last
Cover of the book Social Sciences for an Other Politics by Carsten Last
Cover of the book Combinatorial Algorithms by Carsten Last
Cover of the book The Changing Dynamics of Bisexual Men's Lives by Carsten Last
Cover of the book Python For ArcGIS by Carsten Last
Cover of the book Phytochemicals – Biosynthesis, Function and Application by Carsten Last
Cover of the book Trapping of Small Organisms Moving Randomly by Carsten Last
Cover of the book Advances and Applications of Optimised Algorithms in Image Processing 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