While conventional GIS maps have long been a privileged way for the integration and diffusion of geographical information, novel forms of representation and description of urban ...
Jean-Marie Le Yaouanc, Eric Saux, Christophe Clara...
Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs a...
We develop a new graphical representation for interactive partially observable Markov decision processes (I-POMDPs) that is significantly more transparent and semantically clear t...
The recent ability to measure quickly and inexpensively dense sets of points on physical objects has deeply influenced the way engineers used to represent shapes in CAD systems, ...
Alex Yvart, Stefanie Hahmann, Georges-Pierre Bonne...
We describe and compare three probabilistic ways to perform Content Based Image Retrieval (CBIR) in compressed domain using images in JPEG2000 format. Our main focus are arbitrary ...