Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robust...
ng Abstractions for Local Search Pascal Van Hentenryck1 and Laurent Michel2 1 Brown University, Box 1910, Providence, RI 02912 2 University of Connecticut, Storrs, CT 06269-3155 Ab...
In this paper, we propose a novel region-based active contour model for image segmentation with a variational level set formulation. By introducing a local binary fitting energy, ...
Chunming Li, Chiu-Yen Kao, John C. Gore, Zhaohua D...