Fast algorithms for low-level vision

A computationally efficient recursive filtering structure is presented for smoothing and, calculating the first and second directional derivatives and the Laplacian of an image with a fixed number of operations per output element, independently of the size of the neighborhood considered. It is shown how the recursive approach results on an implementation of low-level vision algorithms that is very efficient in terms of computational effort and how it renders the use of multiresolution techniques very attractive. Applications to edge detection problem are considered, and a novel edge detector allowing zero-crossings of an image, to be extracted with only 14 operations per output element at any resolution is provided. The algorithms have been tested for indoor scenes and noisy images and gave very good results for all of them