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

Institute of Medical Informatics

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

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2017
Editors	K.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff
Number of pages	2
Publisher	Springer Vieweg, Berlin Heidelberg
Publication date	01.03.2017
Pages	215-216
ISBN (Print)	978-3-662-54344-3
ISBN (Electronic)	978-3-662-54345-0
DOIs	https://doi.org/10.1007/978-3-662-54345-0_48
Publication status	Published - 01.03.2017
Event	Bildverarbeitung für die Medizin 2017 - Heidelberg, Germany Duration: 12.03.2017 → 14.03.2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

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

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

Cite this

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 (Eds.), Bildverarbeitung für die Medizin 2017 (pp. 215-216). (Informatik aktuell). Springer Vieweg, Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_48

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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",

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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 (eds), Bildverarbeitung für die Medizin 2017. Informatik aktuell, Springer Vieweg, Berlin Heidelberg, pp. 215-216, Bildverarbeitung für die Medizin 2017
, Heidelberg, Germany, 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. ed. / K.H. Maier-Hein; T.M. Deserno; H. Handels; T. Tolxdorff. Springer Vieweg, Berlin Heidelberg, 2017. p. 215-216 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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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

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SN - 978-3-662-54344-3

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BT - Bildverarbeitung für die Medizin 2017

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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

Abstract

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