The influence of high contrast acuity and normalised low contrast acuity upon self-reported situation avoidance and driving crashes

The aim of this study was to determine the cues used to signal avoidance of difficult driving situations and to test the hypothesis that drivers with relatively poor high contrast visual acuity (HCVA) have fewer crashes than drivers with relatively poor normalised low contrast visual acuity (NLCVA). This is because those with poorer HCVA are well aware of their difficulties and avoid dangerous driving situations while those poorer NLCVA are often unaware of the extent of their problem. Age, self-reported situation avoidance and HCVA were collected during a practice based study of 690 drivers. Screening was also carried out on 7254 drivers at various venues, mainly motorway sites, throughout the UK. Age, self-reported situation avoidance and prior crash involvement were recorded and Titmus vision screeners were used to measure HCVA and NLCVA. Situation avoidance increased in reduced visibility conditions and was influenced by age and HCVA. Only half of the drivers used visual cues to signal situation avoidance and most of these drivers used high rather than low contrast cues. A statistical model designed to remove confounding interrelationships between variables showed, for drivers that did not report situation avoidance, that crash involvement decreased for drivers with below average HCVA and increased for those with below average NLCVA. These relationships accounted for less than 1% of the crash variance, so the hypothesis was not strongly supported. © 2002 The College of Optometrists.

From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.