A series of psychophysical trials is conducted to study the human perception of images of various shapes that are degraded by various combinations of Gaussian blurring, sampling, and fixed pattern noise. These images were created using computer programs designed to simulate the types of image degradation associated with thermal imagers that employ focal plane arrays (FPAs) of detectors. A total of 46 observers participated in these trials, during which over 130,000 observations were recorded. The responses collected during the trials are used to develop an empirical model of human vision applicable to 2-D images having a gray scale, but not including other colors. This model can be used to predict the probability of a human observer correctly distinguishing between images of two different objects.
Non‐birefringent photopolymer materials incorporated into Liquid Crystal Display (LCD) assemblies can improve performance characteristics such as angle of view (AOV), definition, and brightness. Such improvements can be made both to displays that incorporate permanent backlights, and also to those that rely in part on ambient lighting. This paper describes the optical characteristics of some non‐birefringent photopolymer materials, their incorporation into displays, and the consequent improvement in display performance.
A computer model has been developed to predict the probability of recognition of particular shapes when viewed through a thermal imager employing either scanned or focal plane array detectors.This model is based on the results of a series of psychophysical trials during which human observers have considered over 120,000 images of shapes having a range of initial contrasts, and which have been degraded by various combinations of blurring and sampling. These computer generated images were presented to the observers in a random order and with a random degradation, using programmes to select images and display them on a computer monitor. After each presentation the observer decided which was the most likely shape to represent the image displayed on the screen. The responses collected have been used to calculate the human recognition probability of each image. A correlation has been found between the probability of recognition of any specified degraded shape and the relative contrast between the image of that shape, and the image of a similarly degraded circle of the same area. This model has been extended to include the effects of fixed pattern noise and applied to simplified images of cars and vans.
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.
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