The Gaussian derivative model for spatial vision: I. Retinal mechanisms RICHARD A. YOUNG Computer Science Department, General Motors Research Laboratories, Warren, Michigan 48090- 9055, USA Received 9 December 1985; revised 22 May 1987; accepted 30 September 1987 Abstract � Physiological evidence is presented that visual receptive fields in the primate eye are shaped like the sum of a Gaussian function and its Laplacian. A new 'difference-of-offset-Gaussians' or DOOG neural mechanism was identified, which provided a plausible neural mechanism for generating such Gaussian derivative-like fields. The DOOG mechanism and the associated Gaussian derivative model provided a better approximation to the data than did the Gabor or other competing models. A model-free Wiener filter analysis provided independent confirmation of these results. A machine vision system was constructed to simulate human foveal retinal vision, based on Gaussian derivative filters. It provided edge and line enhancement (deblurring) and noise suppression, while retaining all the information in the original image. INTRODUCTION A major trend in image processing is the convergence of biological and machine vision approaches to understanding the basic principles of image-analyzing systems. Insights into how the eye and brain organize visual data may provide novel and powerful computational paradigms for image
Spatial Vision (continued as Seeing & Perceiving from 2010) – Brill
Published: Jan 1, 1987
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