Paper: | IMDSP-L2.2 | ||

Session: | Image and Multidimensional Signal Processing: Theory | ||

Time: | Wednesday, May 19, 15:50 - 16:10 | ||

Presentation: | Lecture | ||

Topic: | Image and Multidimensional Signal Processing: M-D Signal Processing Theory and Methods | ||

Title: | FROM TURBO HIDDEN MARKOV MODELS TO TURBO STATE-SPACE MODELS | ||

Authors: | Florent Perronnin; Institut Eurécom | ||

Jean-Luc Dugelay; Institut Eurécom | |||

Abstract: | We recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2-D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1-D HMMs. In this paper, we consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM). We provide efficient approximate answers to the three following problems: 1) how to compute the likelihood of a set of observations, 2) how to find the sequence of states that best ``explains'' a set of observations and 3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition in the presence of large illumination variations will illustrate the potential of our approach. | ||

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