The Hierarchical Conditional Random Field (HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF m...
Xavier Boix, Josep M. Gonfaus, Joost van de Weijer...
A large number of problems in computer vision can be modeled as energy minimization problems in a markov random field (MRF) framework. Many methods have been developed over the y...
Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by r...
Paul A. Yushkevich, Hongzhi Wang, John Pluta, Bria...
Many cues have been proposed for contour detection or image segmentation. These include low-level image gradients to high-level information such as the identity of the objects in ...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequenc...
Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris...
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...
Active contours, or snakes, are computer-generated curves that move within images to find object boundaries. Its 3D version is often known as deformable models or active surfaces ...
Active contours, or snakes, are computer-generated curves that move within images to find object boundaries. Its 3D version is often known as deformable models or active surfaces ...
Active contours, or snakes, are computer-generated curves that move within images to find object boundaries. Its 3D version is often known as deformable models or active surfaces ...