We introduce a variational approach to image segmentation based on sparse coverings of image domains by shape templates. The objective function combines a data term that achieves ...
Dirk Breitenreicher, Jan Lellmann, Christoph Schn&...
In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation...
Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness ...
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking. Our approach is based on two observations in the cardiac tagged MR ima...