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From global to local statistical sha...

Last, Carsten.

From global to local statistical shape priorsnovel methods to obtain accurate reconstruction results with a limited amount of training shapes /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	From global to local statistical shape priorsby Carsten Last.
其他題名:	novel methods to obtain accurate reconstruction results with a limited amount of training shapes /
作者:	Last, Carsten.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xxi ,259 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Image processingDigital techniques.
電子資源:	http://dx.doi.org/10.1007/978-3-319-53508-1
ISBN:	9783319535081$q(electronic bk.)

From global to local statistical shape priorsnovel 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.3..

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$q(electronic bk.)

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

182119
Image processing
--Digital techniques.

LC Class. No.: TA1637.5

Dewey Class. No.: 006.6

From global to local statistical shape priorsnovel methods to obtain accurate reconstruction results with a limited amount of training shapes /
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