We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
We present a new brain segmentation framework which we apply to T1-weighted magnetic resonance image segmentation. The innovation of the algorithm in comparison to the state-of-the...
Torsten Butz, Patric Hagmann, Eric Tardif, Reto Me...
For decades, Hidden Markov Models (HMMs) have been the state-of-the-art technique for acoustic modeling despite their unrealistic independence assumptions and the very limited rep...
Images and videos can be indexed by multiple features at different levels, such as color, texture, motion, and text annotation. Organizing this information into a system so that u...