Some results on feature detection using residual analysis

Images are considered as consisting of three parts: features, noise, and smooth components. After a smoothing operation, the difference between the result and the original image has the characteristics of noise in areas away from features. Systematic trends in the difference indicate features such as edges, corners, or textures. It is shown that the autocorrelation function of the residuals takes specific forms when computed along various paths, and in particular along a circle or a disk centered at a zero crossing of residuals. Then, feature detection is reduced to classifying the autocorrelation profile. An implementation of this technique is described