Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion

Matthias Wilms; Heinz Handels; Jan Ehrhardt

doi:10.1007/978-3-662-54345-0_48

Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion

Matthias Wilms, Heinz Handels, Jan Ehrhardt

Institut für Medizinische Informatik

Abstract

Statistical shape, appearance, and motion models are widely used as priors in medical image analysis to, for example, constrain image segmentation [1] and motion estimation results [2]. These models try to learn a compact parameterization of the space of plausible object instances from a population of observed samples using low-rank matrix approximation methods (SVD or PCA). The quality of these models heavily depends on the quantity and quality of the training population. As it is usually quite challenging to collect large and representative training populations, models used in practice often suffer from a limited expressiveness

Originalsprache	Englisch
Titel	Bildverarbeitung für die Medizin 2017
Redakteure/-innen	K.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff
Seitenumfang	2
Herausgeber (Verlag)	Springer Vieweg, Berlin Heidelberg
Erscheinungsdatum	01.03.2017
Seiten	215-216
ISBN (Print)	978-3-662-54344-3
ISBN (elektronisch)	978-3-662-54345-0
DOIs	https://doi.org/10.1007/978-3-662-54345-0_48
Publikationsstatus	Veröffentlicht - 01.03.2017
Veranstaltung	Workshop on Bildverarbeitung fur die Medizin 2017 - Heidelberg, Deutschland Dauer: 12.03.2017 → 14.03.2017

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10.1007/978-3-662-54345-0_48

https://www.springer.com/de/book/9783662543443

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Wilms, M., Handels, H., & Ehrhardt, J. (2017). Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion. in K. H. Maier-Hein, T. M. Deserno, H. Handels, & T. Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2017 (S. 215-216). (Informatik aktuell). Springer Vieweg, Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_48

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title = "Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion",

abstract = "Statistical shape, appearance, and motion models are widely used as priors in medical image analysis to, for example, constrain image segmentation [1] and motion estimation results [2]. These models try to learn a compact parameterization of the space of plausible object instances from a population of observed samples using low-rank matrix approximation methods (SVD or PCA). The quality of these models heavily depends on the quantity and quality of the training population. As it is usually quite challenging to collect large and representative training populations, models used in practice often suffer from a limited expressiveness",

author = "Matthias Wilms and Heinz Handels and Jan Ehrhardt",

year = "2017",

month = mar,

day = "1",

doi = "10.1007/978-3-662-54345-0_48",

language = "English",

isbn = "978-3-662-54344-3",

series = "Informatik aktuell",

publisher = "Springer Vieweg, Berlin Heidelberg",

pages = "215--216",

editor = "Maier-Hein, {K.H. } and Deserno, {T.M. } and Handels, {H. } and Tolxdorff, {T. }",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2017",

note = "Bildverarbeitung f{\"u}r die Medizin 2017<br/> ; Conference date: 12-03-2017 Through 14-03-2017",

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Wilms, M, Handels, H & Ehrhardt, J 2017, Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion. in KH Maier-Hein, TM Deserno, H Handels & T Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2017. Informatik aktuell, Springer Vieweg, Berlin Heidelberg, S. 215-216, Workshop on Bildverarbeitung fur die Medizin 2017
, Heidelberg, Deutschland, 12.03.17. https://doi.org/10.1007/978-3-662-54345-0_48

Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion. / Wilms, Matthias; Handels, Heinz ; Ehrhardt, Jan.
Bildverarbeitung für die Medizin 2017. Hrsg. / K.H. Maier-Hein; T.M. Deserno; H. Handels; T. Tolxdorff. Springer Vieweg, Berlin Heidelberg, 2017. S. 215-216 (Informatik aktuell).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

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T1 - Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion

AU - Wilms, Matthias

AU - Handels, Heinz

AU - Ehrhardt, Jan

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Statistical shape, appearance, and motion models are widely used as priors in medical image analysis to, for example, constrain image segmentation [1] and motion estimation results [2]. These models try to learn a compact parameterization of the space of plausible object instances from a population of observed samples using low-rank matrix approximation methods (SVD or PCA). The quality of these models heavily depends on the quantity and quality of the training population. As it is usually quite challenging to collect large and representative training populations, models used in practice often suffer from a limited expressiveness

AB - Statistical shape, appearance, and motion models are widely used as priors in medical image analysis to, for example, constrain image segmentation [1] and motion estimation results [2]. These models try to learn a compact parameterization of the space of plausible object instances from a population of observed samples using low-rank matrix approximation methods (SVD or PCA). The quality of these models heavily depends on the quantity and quality of the training population. As it is usually quite challenging to collect large and representative training populations, models used in practice often suffer from a limited expressiveness

U2 - 10.1007/978-3-662-54345-0_48

DO - 10.1007/978-3-662-54345-0_48

M3 - Conference contribution

SN - 978-3-662-54344-3

T3 - Informatik aktuell

SP - 215

EP - 216

BT - Bildverarbeitung für die Medizin 2017

A2 - Maier-Hein, K.H.

A2 - Deserno, T.M.

A2 - Handels, H.

A2 - Tolxdorff, T.

PB - Springer Vieweg, Berlin Heidelberg

T2 - Bildverarbeitung für die Medizin 2017<br/>

Y2 - 12 March 2017 through 14 March 2017

ER -

Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion

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