Steerable kernels for arbitrarily-sampled spaces

This paper describes a procedure for generating steerable kernels corresponding to general image mappings (e.g., non-rigid transformations) for arbitrary image tessellations. The paper presents the basic components that can efficiently rotate functions over arbitrary samplings of an image space, including rectilinear grids and irregularly - spaced pixels.