: We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sa...
We present a novel local region approach for statistically characterizing appearance in the context of medical image segmentation via deformable models. Our appearance model refl...
Joshua Stough, Robert E. Broadhurst, Stephen M. Pi...
Motivated by the principle of agnostic learning, we present an extension of the model introduced by Balcan, Blum, and Gupta [3] on computing low-error clusterings. The extended mod...
A generalized image model (GIM) is presented. Images are represented as sets of four-dimensional (4D) sites combining position and intensity information, as well as their associat...
Abstract. We present a novel statistical-model-based segmentation algorithm that addresses a recurrent problem in appearance model fitting and model-based segmentation: the "s...