Mach Bands: multi-scale spatial filtering and co-operative coding of edges and bars

Perception of Mach bands may be explained by spatial filtering ('lateral inhibition') that can be approximated by 2nd derivative computation, and several alternative models have been proposed. To distinguish between them, we used a novel set of ‘generalised Gaussian’ images, in which the sharp ramp-plateau junction of the Mach ramp was replaced by smoother transitions. The images ranged from a slightly blurred Mach ramp to a Gaussian edge and beyond, and also included a sine-wave edge. The probability of seeing Mach Bands increased with the (relative) sharpness of the junction, but was largely independent of absolute spatial scale. These data did not fit the predictions of MIRAGE, nor 2nd derivative computation at a single fine scale. In experiment 2, observers used a cursor to mark features on the same set of images. Data on perceived position of Mach bands did not support the local energy model. Perceived width of Mach bands was poorly explained by a single-scale edge detection model, despite its previous success with Mach edges (Wallis & Georgeson, 2009, Vision Research, 49, 1886-1893). A more successful model used separate (odd and even) scale-space filtering for edges and bars, local peak detection to find candidate features, and the MAX operator to compare odd- and even-filter response maps (Georgeson, VSS 2006, Journal of Vision 6(6), 191a). Mach bands are seen when there is a local peak in the even-filter (bar) response map, AND that peak value exceeds corresponding responses in the odd-filter (edge) maps.

Dehydration at the lens surface:surface energy and surfactant persistence

Purpose: Most published surface wettability data are based on hydrated materials and are dominated by the air-water interface. Water soluble species with hydrophobic domains (such as surfactants) interact directly with the hydrophobic domains in the lens polymer. Characterisation of relative polar and non-polar fractions of the dehydrated material provides an additional approach to surface analysis. Method: Probe liquids (water and diiodomethane) were used to characterise polar and dispersive components of surface energies of dehydrated lenses using the method of Owens and Wendt. A range of conventional and silicone hydrogel soft lenses was studied. The polar fraction (i.e. polar/total) of surface energy was used as a basis for the study of the structural effects that influence surfactant persistence on the lens surface. Results: When plotted against water content of the hydrated lens, polar fraction of surface energy (PFSE) values of the dehydrated lenses fell into two rectilinear bands. One of these bands covered PFSE values ranging from 0.4 to 0.8 and contained only conventional hydrogels, with two notable additions: the plasma coated silicone hydrogels lotrafilcon A and B. The second band covered PFSE values ranging from 0.04 to 0.28 and contained only silicone hydrogels. Significantly, the silicone hydrogel lenses with lowest PFSE values (p<0.15) are found to be prone to lipid deposition duringwear. Additionally, more hydrophobic surfactants were found to be more persistent on lenses with lower PFSE values. Conclusions: Measurement of polar fraction of surface energy provides an importantmechanistic insight into surface interactions of silicone hydrogels.