東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

From global to local statistical sha...

Last, Carsten.

FindBook

Google Book

Amazon

博客來

From global to local statistical shape priors = novel 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 priors/ by 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.
內容註:	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
標題:	Image processing - Digital techniques. -
電子資源:	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 /
LDR:02196nmm a2200325 a 4500 001 2092847
003 DE-He213
005 20170914120451.0
006 m d
007 cr nn 008maaau
008 171117s2017 gw s 0 eng d
020 $a 9783319535081 $q (electronic bk.)
020 $a 9783319535074 $q (paper)
024 7 $a 10.1007/978-3-319-53508-1 $2 doi
035 $a 978-3-319-53508-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1637.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.6 $2 23
090 $a TA1637.5 $b .L349 2017
100 1 $a Last, Carsten. $3 3227132
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.
650 0 $a Image processing $x Digital techniques. $3 532550
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in systems, decision and control ; $v v.98. $3 3227133
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-53508-1
950 $a Engineering (Springer-11647)