From Global to Local Statistical Shape Priors (Record no. 80294)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03253nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-53508-1 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801222013.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 170314s2017 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319535081 |
| -- | 978-3-319-53508-1 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Last, Carsten. |
| 245 10 - TITLE STATEMENT | |
| Title | From Global to Local Statistical Shape Priors |
| Sub Title | Novel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XXI, 259 p. 84 illus., 64 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Studies in Systems, Decision and Control, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-53508-1 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2017. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Image processing—Digital techniques. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2198-4190 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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