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...
Recent progress in per-pixel object class labeling of natural images can be attributed to the use of multiple types of image features and sound statistical learning approaches. Wit...
We present a novel representation for modeling textured
regions subject to smooth variations in orientation and
scale. Utilizing the steerable pyramid of Simoncelli and
Freeman ...
Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal di...
A novel method for the segmentation of multiple objects from 3D medical images using inter-object constraints is presented. Our method is motivated by the observation that neighbor...