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

Conference paper
Part of the Informatik aktuell book series (INFORMAT)

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

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckDeutschland

Personalised recommendations