A HMM approach to the estimation of random trajectories on manifolds (Q1607248)

Dynamic image analysis requires the estimation of time-varying model parameters (e.g., shape coefficients). This can be seen as states of a dynamic model which are restricted to a subset of Euclidean space. This paper describes an algorithm for the estimation of the state evolution on manifolds exploiting three sources of information: the manifold geometry, the motion model and the sensor model. Examples are provided to illustrate the performance of this method in situations where classic procedures cannot perform well.