Stochastic analysis of scale-space smoothing

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. This paper is based on two key ideas: to investigate the stochastic properties of scale-space representations, and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors.