<title>Visual recognition of blurred shapes</title>

Abstract

The human recognition probabilities of blurred rotationally symmetric shapes have been studied using computer generated images displayed in a 128x128 pixel area on a T.V. monitor.The shapes employed included a series of regular polygons, crosses and rectangles. These were blurred by convolution with two dimensional Gaussian functions which had standard deviations ranging from 0 to 29. Images of the blurred shapes were presented to observers in a random order and with a random extent of blurring. After each presentation the observer decided which of the shapes was most likely to be represented by the image displayed on the screen. A correlation has been found between the extent by which a shape may be blurred before it ceases to be recognizable and the difference between the original shape and a circle of the same area. This correlation has been expressed as an empirical relationship between the probability of recognition and the standard deviation of the Gaussian blurring function when the latter is normalised by a function which depends on the original shape and the one it is being confused with. This relationship has been applied to a series of irregular shapes to predict the amount of blurring required before they too cease to be recognizable. These predictions have been compared to experimental observations for the irregular shapes considered.The probability of recognizing an image may be used as a measure of image quality. The empirical relationship derived from this work could, therefore, form the basis of a new objective performance measure for thermal imaging systems.