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Statistical models have opened up new possibilities for the automated analysis of images. However, the limited availability of representative training data, e.g. segmented images, leads to a bottleneck for the application of statistical models in practice. In this paper, we propose a novel patch-based technique that enables to learn representative statistical models of shape, appearance, or motion with a high grade of detail from a small number of observed training samples using lowrank matrix completion methods. Our method relies on the assumption that local variations have limited effects in distant areas. We evaluate our approach on three exemplary applications: (1) 2D shape modeling of faces, (2) 3D modeling of human lung shapes, and (3) population-based modeling of respiratory organ deformation. A comparison with the classical PCA-based modeling approach and FEM-PCA shows an improved generalization ability for small training sets indicating the improved flexibility of the model.
|Title of host publication||Computer Vision - 14th European Conference, ECCV 2016, Proceedings|
|Number of pages||16|
|Publication status||Published - 01.01.2016|
|Event||14th European Conference on Computer Vision (ECCV) 2016|
- Amsterdam, Netherlands
Duration: 08.10.2016 → 16.10.2016
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