RESIDUAL ANALYSIS FOR FEATURE DETECTION

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 textured areas. We show 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.