Abstract
In the present study, we investigated the detection of contours defined by constant curvature and the statistics of curved contours in natural scenes. In Experiment 1, we examined the degree to which human sensitivity to contours is affected by changing the curvature angle and disrupting contour curvature continuity by varying the orientation of end elements. We find that (1) changing the angle of contour curvature decreased detection performance, while (2) end elements oriented in the direction (i.e., clockwise) of curvature facilitated contour detection regardless of the curvature angle of the contour. In Experiment 2 we further established that the relative effect of end—element orientation on contour detection was not only dependent on their orientation (collinear or cocircular), but also their spatial separation from the contour, and whether the contour shape was curved or not (i.e., C-shaped or S-shaped). Increasing the spatial separation of end-elements reduced contour detection performance regardless of their orientation or the contour shape. However, at small separations, cocircular end-elements facilitated the detection of C-shaped contours, but not S-shaped contours. The opposite result was observed for collinear end-elements, which improved the detection of S- shaped, but not C-shaped contours. These dissociative results confirmed that the visual system specifically codes contour curvature, but the association of contour elements occurs locally. Finally, we undertook an analysis of natural images that mapped contours with a constant angular change and determined the frequency of occurrence of end elements with different orientations. Analogous to our behavioral data, this image analysis revealed that the mapped end elements of constantly curved contours are likely to be oriented clockwise to the angle of curvature. Our findings indicate that the visual system is selectively sensitive to contours defined by constant curvature and that this might reflect the properties of curved contours in natural images.
Highlights
In visual scenes, contours are a collection of spatially linked line elements that are associated based on a small number of common properties such as regular continuity, orientation, depth, closure, and proximity
Note that the orientation of end elements resulting in the greatest detectability was slightly less than the contour angle. These results suggests a detection bias toward curved contours: end elements that are oriented in the contour angle direction are more detectable relative to the opposite direction, and this finding is in agreement with previous work (e.g., Pettet et al, 1998; Mathes and Fahle, 2007) showing that fewer changes of direction along a contour produces better detection performance
In Experiment 1 we showed that sensitivity to curved contours decreases as a function of curvature angle and is dependent on the orientation of end contour elements
Summary
Contours are a collection of spatially linked line elements that are associated based on a small number of common properties such as regular continuity, orientation, depth, closure, and proximity (see e.g., Marr, 1982; Field et al, 1993; Kovacs and Julesz, 1993; Khuu et al, 2015, 2016). This work introduced the concept of a contour association-field to model the optimal relationship between local edge-elements in which contour grouping occurs This model holds that the integration of contours is underpinned by the principle of “good-continuation,” such that a train of local pairwise contour elements are likely to be grouped to form a contour if they are placed in close proximity to each other, have the same (or similar orientation tuned to a particular range), polarity, and phase; pairwise edge elements that largely differ in these properties are not grouped. Kovacs and Julesz (1993) reported a detection advantage to closed contour stimuli compared to those with open structures (see Mathes and Fahle, 2007; c.f., Tversky et al, 2004) This preference to closed contour might reflect the fact that real objects and salient features in the visual world (such as fruits and faces), which tend to have complete edge structures
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