Conditional Iterative Decoding of Two Dimensional Hidden Markov Models
Two Dimensional Hidden Markov Models (2D-HMMs) find many applications on the field of computer vision/image analysis. In this talk, I am going to explain a new algorithm for decoding of 2D-HMMs. The proposed algorithm consists of conditional iterative updates that "communicate" through the joint posterior state probabilities. We also show that the proposed algorithm gives promising results on both synthetic and deformable face recognition scenarios.
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