We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
Markov random field (MRF, CRF) models are popular in
computer vision. However, in order to be computationally
tractable they are limited to incorporate only local interactions
a...
This study investigates variational image segmentation with an original data term, referred to as statistical overlap prior, which measures the conformity of overlap between the no...
We develop data structures for dynamic closest pair problems with arbitrary (not necessarily geometric) distance functions, based on a technique previously used by the author for ...
We present an approach to spatial inference which is based on the procedural semantics of spatial relations. In contrast to qualitative reasoning, we do not use discrete symbolic m...
Sylvia Wiebrock, Lars Wittenburg, Ute Schmid, Frit...