Contour detection threshold: repeatability and learning with 'contour cards'

Abstract

Human observers are able to locate contours that are defined solely on the basis of longrange, orientation-domain correlations. The integrity of the mechanisms responsible for second-order contour detection is disrupted by amblyopia (Kovacs et al., 1996; Hess et al., 1997) and it is therefore of interest to develop methods for assessing pediatric patients undergoing treatment for amblyopia. In this study, we have determined the inter-observer and test-retest reliability of a card-based test of second-order contour integration. The magnitude of practice effects was also assessed in both adult and pediatric patient groups. Contour detection thresholds were measured for a closed contour, defined by Gabor patches, embedded in a randomly oriented Gabor-patch background. The visibility of the contour was controlled by varying the density of the background elements. Thresholds, defined in terms of the ratio of contour element spacing to average background spacing were measured with a clinical staircase procedure. Thresholds measured by two observers differed on average by 0.023 ± 0.075 or about one half the increment between cards. Children and adults showed only small practice effects (0.022 ± 0.051 vs 0.053 ± 0.077, respectively) and average unsigned differences between repeated measures were equivalent to approximately 1 card across groups. A card-based test of second-order contour integration produces reliable estimates of contour integration performance in normal and amblyopic observers, including children.

Development of sensitivity to visual motion in macaque monkeys

“…More complex perceptual abilities requiring integration of information over space have more extended developmental time courses. For example, children younger than about age 3 are unable to perform a contour integration task, and their performance continues to improve into the teenage years (Pennefather et al, 1999;Kovács et al, 1999). Similarly, monkeys are unable to perform contour integration before 5-6 months and continue to improve into at least the second postnatal year (Kiorpes & Bassin, 2003).…”

How long range is contour integration in human color vision?

“…Previous studies on contour integration have either investigated the effect of relative densities of contour and background elements on contour saliency (Kovacs & Julesz, 1993;Kovacs et al, 1999;Pennefather et al, 1999), or looked at the effect of curvature, contrast, and chromaticity in dense arrays where no relative density is present between contour and background elements (Field et al, 1993;McIlhagga & Mullen, 1996;Mullen et al, 2000). In the former task, the cue for contour detection is mainly a difference in density, while in the latter contour detection is subserved by orientation continuity, better corresponding to a contour integration task.…”

Perceptually Motivated Image Features Using Contours

“…Two categories were motivated by the information commonly used by existing texture features: 2nd-order statistics, and short-range higher order statistics (HOS) (typically obtained from image patches). The use of the third category, contour data, was motivated by the fact that the human visual system is extremely adept at exploiting these visual cues [25], [44], [46][47], [53] and that they utilise long-range HOS. We conducted an experiment with human observers that showed that for the Pertex database [29], contours are the most useful category of data for human texture discrimination.…”

“…These images are outlined using the above colour scheme to indicate which property is most important to their recognition: contour maps (red), 2nd-order statistics contained in phase-randomised images (blue) or the short-range interactions contained in randomised blocked images (cyan). [46][47]. In addition, it has been shown that objects can be identified using discontinuous fragmented contour segments [44].…”

“…It is well-known, however, that these characteristics have an important role in human visual perception [15], [25], [42], [44], [53], [55]. For example, human observers often cannot recognise an aperiodic image when its phase spectrum is scrambled and its power spectrum is kept intact [42], but they are able to exploit the long-range visual interactions evident in contour information [25], [44], [46][47], [53]. Design of perceptually inspired computer algorithms has been studied in the community [2] but, to our knowledge, no research has been reported which utilises contour data 2 for texture analysis.…”