Training CNNs for Image Registration from Few Samples with Model-based Data AugmentationTraining CNNs for Image Registration from Few Samples with Model-based Data AugmentationUzunova, Hristina and Wilms, Matthias and Handels, Heinz and Ehrhardt, Jan2017
Paper summaryjoecohenThe authors state that the usual approach to cope with few training samples is data augmentation. They extend a method of modelling the data from \cite{10.1016/j.media.2017.02.003} and use it to train a neural network. The figure below shows the overview:
https://i.imgur.com/joLNyfc.png
At the core of deformation model they determine a set of $m$ landmarks $s_i$ which they will deform and then perform an affine transformation to warp the image to align to these points. The points are moved in a constrained way. They state the constraint is a "multi-level B-spline scattered data approximation".
Here is the poster: https://i.imgur.com/enQQqxC.png
The authors state that the usual approach to cope with few training samples is data augmentation. They extend a method of modelling the data from \cite{10.1016/j.media.2017.02.003} and use it to train a neural network. The figure below shows the overview:
https://i.imgur.com/joLNyfc.png
At the core of deformation model they determine a set of $m$ landmarks $s_i$ which they will deform and then perform an affine transformation to warp the image to align to these points. The points are moved in a constrained way. They state the constraint is a "multi-level B-spline scattered data approximation".
Here is the poster: https://i.imgur.com/enQQqxC.png