Optical flow and advection on 2-Riemannian manifolds: A common framework
Dynamic pattern analysis and motion extraction can be efficiently addressed using optical flow techniques. This paper presents a generalization of these questions to nonflat surfaces, where optical flow is tackled through the problem of evolution processes on non-euclidean domains. The classical equations of optical flow in the euclidean case are transposed to the theoretical framework of differential geometry. We adopt this formulation for the regularized optical flow problem, prove its mathematical well posedness, and combine it with the advection equation. The optical flow and advection problems are dual: A motion field may be retrieved from some scalar evolution using optical flow; conversely, a scalar field may be deduced from a velocity field using advection. These principles are illustrated with qualitative and quantitative evaluations from numerical simulations bridging both approaches. The proof-of-concept is further demonstrated with preliminary results from time-resolved functional brain imaging data, where organized propagations of cortical activation patterns are evidenced using our approach.
optical flow advection differential geometry brain imaging propagation partial differential equations riemannian manifold finite element method meg eeg