Since the work by Osher and Sethian on level-sets algorithms for numerical shape evolutions, this technique has been used for a large number of applications in numerous fields. In...
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...
A computationally efficient approach to local learning with kernel methods is presented. The Fast Local Kernel Support Vector Machine (FaLK-SVM) trains a set of local SVMs on redu...
The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape r...
Jasjit S. Suri, Kecheng Liu, Sameer Singh, Swamy L...
In this paper we articulate a new modeling paradigm for both local and global editing on complicated point set surfaces of arbitrary topology. In essence, the proposed technique l...