Mach edges: a critical test of the nonlinear 3rd derivative model for edge-detection

Feature detection is a crucial stage of visual processing. In previous feature-marking experiments we found that peaks in the 3rd derivative of the luminance profile can signify edges where there are no 1st derivative peaks nor 2nd derivative zero-crossings (Wallis and George 'Mach edges' (the edges of Mach bands) were nicely predicted by a new nonlinear model based on 3rd derivative filtering. As a critical test of the model, we now use a new class of stimuli, formed by adding a linear luminance ramp to the blurred triangle waves used previously. The ramp has no effect on the second or higher derivatives, but the nonlinear model predicts a shift from seeing two edges to seeing only one edge as the added ramp gradient increases. In experiment 1, subjects judged whether one or two edges were visible on each trial. In experiment 2, subjects used a cursor to mark perceived edges and bars. The position and polarity of the marked edges were close to model predictions. Both experiments produced the predicted shift from two to one Mach edge, but the shift was less complete than predicted. We conclude that the model is a useful predictor of edge perception, but needs some modification.

The mosaic test:measuring the effectiveness of colour-based image retrieval

A variety of content-based image retrieval systems exist which enable users to perform image retrieval based on colour content - i.e., colour-based image retrieval. For the production of media for use in television and film, colour-based image retrieval is useful for retrieving specifically coloured animations, graphics or videos from large databases (by comparing user queries to the colour content of extracted key frames). It is also useful to graphic artists creating realistic computer-generated imagery (CGI). Unfortunately, current methods for evaluating colour-based image retrieval systems have 2 major drawbacks. Firstly, the relevance of images retrieved during the task cannot be measured reliably. Secondly, existing methods do not account for the creative design activity known as reflection-in-action. Consequently, the development and application of novel and potentially more effective colour-based image retrieval approaches, better supporting the large number of users creating media for use in television and film productions, is not possible as their efficacy cannot be reliably measured and compared to existing technologies. As a solution to the problem, this paper introduces the Mosaic Test. The Mosaic Test is a user-based evaluation approach in which participants complete an image mosaic of a predetermined target image, using the colour-based image retrieval system that is being evaluated. In this paper, we introduce the Mosaic Test and report on a user evaluation. The findings of the study reveal that the Mosaic Test overcomes the 2 major drawbacks associated with existing evaluation methods and does not require expert participants. © 2012 Springer Science+Business Media, LLC.

Mobile app Aston contrast sensitivity test

BACKGROUND: Contrast detection is an important aspect of the assessment of visual function; however, clinical tests evaluate limited spatial frequencies and contrasts. This study validates the accuracy and inter-test repeatability of a swept-frequency near and distance mobile app Aston contrast sensitivity test, which overcomes this limitation compared to traditional charts. METHOD: Twenty subjects wearing their full refractive correction underwent contrast sensitivity testing on the new near application (near app), distance app, CSV-1000 and Pelli-Robson charts with full correction and with vision degraded by 0.8 and 0.2 Bangerter degradation foils. In addition repeated measures using the 0.8 occluding foil were taken. RESULTS: The mobile apps (near more than distance, p = 0.005) recorded a higher contrast sensitivity than printed tests (p < 0.001); however, all charts showed a reduction in measured contrast sensitivity with degradation (p < 0.001) and a similar decrease with increasing spatial frequency (interaction > 0.05). Although the coefficient of repeatability was lowest for the Pelli-Robson charts (0.14 log units), the mobile app charts measured more spatial frequencies, took less time and were more repeatable (near: 0.26 to 0.37 log units; distance: 0.34 to 0.39 log units) than the CSV-1000 (0.30 to 0.93 log units). The duration to complete the CSV-1000 was 124 ± 37 seconds, Pelli-Robson 78 ± 27 seconds, near app 53 ± 15 seconds and distance app 107 ± 36 seconds. CONCLUSIONS: While there were differences between charts in contrast levels measured, the new Aston near and distance apps are valid, repeatable and time-efficient method of assessing contrast sensitivity at multiple spatial frequencies.