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arXiv · 2101.03801

Hidden Markov chains and fields with observations in Riemannian manifolds

Salem Said·Nicolas Le Bihan·Jonathan H. Manton
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Abstract

Hidden Markov chain, or Markov field, models, with observations in a Euclidean space, play a major role across signal and image processing. The present work provides a statistical framework which can be used to extend these models, along with related, popular algorithms (such as the Baum-Welch algorithm), to the case where the observations lie in a Riemannian manifold. It is motivated by the potential use of hidden Markov chains and fields, with observations in Riemannian manifolds, as models for complex signals and images.

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Salem Said, Nicolas Le Bihan, Jonathan H. Manton. 2021-01-11. Hidden Markov chains and fields with observations in Riemannian manifolds. https://arxiv.org/abs/2101.03801

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