We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Supplying realistically textured 3D city models at ground level promises to be useful for pre-visualizing upcoming traffic situations in car navigation systems. Because this previs...
Nico Cornelis, Bastian Leibe, Kurt Cornelis, Luc J...
We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns...
Florent Monay, Pedro Quelhas, Daniel Gatica-Perez,...
— In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene g...
Eren Erdal Aksoy, Alexey Abramov, Florentin Wö...
We seek a framework that addresses localization, detection and recognition of man-made objects in natural-scene images in a unified manner. We propose to model artificial structur...