Michelson's contrast, C, is an excellent metric for contrast in images with periodic luminance profiles, such as gratings, but is not suitable for images consisting of isolated stimulus elements, eg single bars; other metrics have been devised for such stimuli. But what metric should be used for random-dot images such as are commonly used in stereograms and kinematograms? Previously the standard deviation (SD) of the luminances (equivalent to the root mean square, RMS, of the amplitudes) has been taken as a measure of contrast, but on little more than intuitive grounds. The validity of this speculative usage is tested. Experiments are described in which a wide range of random-dot images of various compositions was used and the adapting power of these images measured. This was taken as an index of their visual effectiveness. The contrast and contrast-reducing effects of the stimuli were expressed in terms of six candidate metrics, including SD, to discover which would give the most lawful description of the experimental data. The usefulness and generality of the SD measure were confirmed. The effects of mean luminance were also measured and a general expression that would take them into account was derived. Finally, on the basis of computational modelling in which spatial filters with properties approximating those of retinal ganglion cells were used, a possible theoretical account for the success of the SD metric is offered.

When two superimposed moving dot fields are presented simultaneously, their axes of movement appear shifted away from each other. The shift only occurs when the two directions are within 90° of each other, and is directly comparable to that which results from adapting to one and testing on the other direction. This effect is taken as further evidence for a distribution-shift model in the direction domain. It is argued that the currently accepted model of movement detection, which restricts itself to comparisons only between oppositely-tuned direction detectors, should be elaborated to include comparisons across a very wide range of detectors.

No abstract

We have measured the sensitivity of the human visual system to sinusoidal modulations of orientation in micropattern-based textured stimuli. The result is the orientation modulation function, or OMF, which describes this sensitivity as a function of the spatial frequency of orientation modulation. We found that the OMF was bandpass with peak sensitivity at spatial frequencies ranging between 0.06 and 0.2 c/deg, depending on the size of the micropatterns. The OMF was found to be scale invariant, that is its position on the spatial frequency axis did not change with viewing distance when spatial frequency was measured in object rather than retinal units. This scale invariance was shown to result from the visual system taking into account the scale rather than the density of the micropatterns as viewing distance was changed. It has been argued by Bergen [(1991) Vision and visual dysfunction (Vol. 10B) New York: Macmillan] that scale invariance in textures is a consequence of the coupling of mechanisms which detect textural features with those which detect local luminance contrasts. We reasoned that Gabor micropattern textures might therefore show narrower OMFs compared to line micropattern textures. However we found no difference in OMF bandwidth between the Gabor and line micropattern textures, suggesting that the line micropatterns were acting as selectively as the Gabor micropatterns for the spatial scale of the mechanisms which detected the orientation modulation. Evidence is presented which suggests that the mechanisms which detected the orientation modulation in our stimuli are non-linear. Finally we showed similar OMFs for sine-wave and square-wave modulations of micropattern orientation, and similar OMFs for modulations of micropattern with orientation about the horizontal and about the vertical, the direction of modulation in both cases being horizontal. The implications of these findings for the mechanisms involved in orientation-defined texture processing is discussed.

A model of brightness coding is presented which is shown to predict the appearance of a number of classical brightness phenomena. The model is known as MIDAAS which stands for Multiple Independent Descriptions Averaged Across Scale. In common with many other approaches to brightness perception MIDAAS imputes to local feature detectors a central role in the computation of brightness. It also explicitly recognises the crucial importance to brightness perception of feature detectors operating at different spatial scales. The unique and definitive feature of the model however is the supposition that each scale of spatial filtering operates as if to generate its own description of the pattern of brightness relationships in the image. The final percept is then provided by the composite of those individual brightness descriptions. It is shown that MIDAAS provides a good account of a variety of Mach band phenomena, the conditions under which the Missing Fundamental illusion is observed, the effect of occluding bars on the apparent contrast of step edges, the Chevreul illusion, simultaneous brightness contrast and the non-linear appearance of high contrast sinusoidal gratings. The advantages of MIDAAS over other approaches to brightness perception is discussed, as well as its current limitations.