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From global to local statistical sha...

Last, Carsten.

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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 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	From global to local statistical shape priors/ by Carsten Last.
Reminder of title:	novel methods to obtain accurate reconstruction results with a limited amount of training shapes /
Author:	Last, Carsten.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxi ,259 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Image processing - Digital techniques. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-53508-1
ISBN:	9783319535081

From global to local statistical shape priors = novel methods to obtain accurate reconstruction results with a limited amount of training shapes /
Last, Carsten.

From global to local statistical shape priorsnovel methods to obtain accurate reconstruction results with a limited amount of training shapes /[electronic resource] :by Carsten Last. - Cham :Springer International Publishing :2017. - xxi ,259 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.982198-4182 ;. - Studies in systems, decision and control ;v.98..

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.

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.

ISBN: 9783319535081

Standard No.: 10.1007/978-3-319-53508-1doiSubjects--Topical Terms:

532550
Image processing
--Digital techniques.

LC Class. No.: TA1637.5

Dewey Class. No.: 006.6

From global to local statistical shape priors = novel methods to obtain accurate reconstruction results with a limited amount of training shapes /
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245 1 0 $a From global to local statistical shape priors $h [electronic resource] : $b novel methods to obtain accurate reconstruction results with a limited amount of training shapes / $c by Carsten Last.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xxi ,259 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.98
505 0 $a 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.
520 $a 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.
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650 1 4 $a Engineering. $3 586835
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650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
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