From error probability to information theoretic (multi-modal) signal processing

We propose an information theoretic model that unifies a wide range of existing information theoretic signal processing algorithms in a compact mathematical framework. It is mainly based on stochastic processes, Markov chains and error probabilities. The proposed framework will allow us to discuss revealing analogies and differences between several well-known algorithms and to propose interesting extensions resulting directly from our formalism. We will then describe how the theory can be applied to the rapidly emerging field of multi-modal signal processing: we will show how our framework can be efficiently used for multi-modal medical image processing and for joint analysis of multi-media sequences (audio and video).