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

Matthias Wilms, Heinz Handels, Jan Ehrhardt

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 languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2017
EditorsK.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff
Number of pages2
PublisherSpringer Vieweg, Berlin Heidelberg
Publication date01.03.2017
Pages215-216
ISBN (Print)978-3-662-54344-3
ISBN (Electronic)978-3-662-54345-0
DOIs
Publication statusPublished - 01.03.2017
EventBildverarbeitung für die Medizin 2017
- Heidelberg, Germany
Duration: 12.03.201714.03.2017

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