Computational coding theory for image alignment and identification by human observers

Although sufficient evidence exists for the perceptual decomposition of spatial information via essentially uncorrected detector arrays with specific signals, their general form and applicability to superthreshold spatial vision is still questionable. In this paper we propose a nonlinear adaptive matched filter model for spatial coding which not only estimates the underlying detector characteristics to represent the processes of image alignment and identification, but also produces quantitative bounds on the efficiency of the decision processes based on same. Of particular importance to predicting results or visual texture discrimination, alignment of images, detection of signals embedded in scenes, and edge extraction are (1) the apparent nonlinear thresholding activity of such detectors, (2) the importance of the gradient or edge response, and (3) the use of cross correlation or matching as a comparison procedure. All processes are illustrated and compared with human psychophysical data. This formulation for superthreshold identification phenomena reformulates image amplitude and phase registration in terms of underlying (derivative) matched filter responses.