We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D spac...
Abstract--Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining appro...
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu, Geoff...
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local...
Θk-graphs are geometric graphs that appear in the context of graph navigation. The shortest-path metric of these graphs is known to approximate the Euclidean complete graph up to...
Nicolas Bonichon, Cyril Gavoille, Nicolas Hanusse,...
Abstract. We propose a learning method which introduces explicit knowledge to the object correspondence problem. Our approach uses an a priori learning set to compute a dense corre...