Abstract

This paper describes an algorithm for distinguishing between images of two classes of objects: artificial and natural. An approximation to the image is generated using graphical elements similar to the receptor fields of neurons in the primary visual cortex (Zone VI). We show that the approximation-error distribution for natural-object images lies at higher values of the approximation error than that for artificial-object images. This difference makes it possible to detect artificial objects against natural backgrounds.

© 2018 Optical Society of America

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