Orthonormal Diffusion Decompositions of Images for Optical Flow Estimation

This paper proposes an ortho-diffusion decomposition of graphs for estimating motion from image sequences. Orthonormal decompositions of the adjacency matrix representations of image data are alternated with diffusions and data subsampling in order to robustly represent image features using undirected graphs. Modified Gram-Schmidt with pivoting the columns algorithm is applied recursively for the orthonormal decompositions at various scales. This processing produces a set of ortho-diffusion bases and residual diffusion wavelets at each image representation scale. The optical flow is estimated using the similarity in the ortho-diffusion bases space extracted from regions of two different image frames.