SUMMARY1. The perception of contrast was measured in humans by a technique of subjective contrast-matching, and was compared with contrast sensitivity as defined by threshold measures.2. Contrast-matching between different spatial frequencies was performed correctly (especially at frequencies above 5 c/deg) despite the attenuation by optical and neural factors which cause large differences in contrast thresholds.3. Contrast-matching between single lines of different widths was also veridical, and was not limited by the spatial integration (Ricco's Law) present at threshold. Adaptation to gratings altered the appearance of lines, and this could be best understood in Fourier terms.4. The generality of these results was shown by matching the contrast of pictures which had been filtered so that each contained a one octave band of spatial frequencies.5. Within the limits imposed by threshold and resolution, contrastmatching was largely independent of luminance and position on the retina.6. Six out of eleven astigmatic observers showed considerable suprathreshold compensation for their orientation-specific neural deficit in contrast sensitivity.7. These results define a new property of vision: contrast constancy. It is argued that spatial frequency channels in the visual cortex are organized to compensate for earlier attenuation. This achieves a dramatic 'deblurring' of the image, and optimizes the clarity of vision.
Recent work on the visual interpretation of traffic scenes is described which relies heavily on a
priori
knowledge of the scene and position of the cam era, and expectations about the shapes of vehicles and their likely movements in the scene. Knowledge is represented in the computer as explicit three-dimensional geometrical models, dynamic filters, and descriptions of behaviour. Model-based vision, based on reasoning with analogue models, avoids many of the classical problems in visual perception: recognition is robust against changes in the image of shape, size, colour and illumination. The three-dimensional understanding of the scene which results also deals naturally with occlusion, and allows the behaviour of vehicles to be interpreted. The experiments with machine vision raise questions about the part played by perceptual context for object recognition in natural vision, and the neural mechanisms which might serve such a role.
The paper reports an interactive tool for calibrating a camera, suitable for use in outdoor scenes. The motivation for the tool was the need to obtain an approximate calibration for images taken with no explicit calibration data. Such images are frequently presented to research laboratories, especially in surveillance applications, with a request to demonstrate algorithms. The method decomposes the calibration parameters into intuitively simple components, and relies on the operator interactively adjusting the parameter settings to achieve a visually acceptable agreement between a rectilinear calibration model and his own perception of the scene. Using the tool, we have been able to calibrate images of unknown scenes, taken with unknown cameras, in a matter of minutes. The standard of calibration has proved to be sufficient for model-based pose recovery and tracking of vehicles.
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