Natural statistics of depth edges modulate perceptual stability

Abstract

Binocular fusion relies on matching points in the two eyes that correspond to the same physical feature in the world; however, not all world features are binocularly visible. Near depth edges, some regions of a scene are often visible to only one eye (so-called half occlusions). Accurate detection of these monocularly visible regions is likely to be important for stable visual perception. If monocular regions are not detected as such, the visual system may attempt to binocularly fuse non-corresponding points, which can result in unstable percepts. We investigated the hypothesis that the visual system capitalizes on statistical regularities associated with depth edges in natural scenes to aid binocular fusion and facilitate perceptual stability. By sampling from a large set of stereoscopic natural images with co-registered distance information, we found evidence that monocularly visible regions near depth edges primarily result from background occlusions. Accordingly, monocular regions tended to be more visually similar to the adjacent binocularly visible background region than to the adjacent binocularly visible foreground. Consistent with our hypothesis, perceptual experiments showed that perception tended to be more stable when the image properties of the depth edge were statistically more likely given the probability of occurrence in natural scenes (i.e., when monocular regions were more visually similar to the binocular background). The generality of these results was supported by a parametric study with simulated environments. Exploiting regularities in natural environments may allow the visual system to facilitate fusion and perceptual stability when both binocular and monocular regions are visible. world, the visual system exploits statistical regularities that are present in binocular images of natural scenes

Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model

“…Depth differences at object boundaries may give rise to areas of binocular half-occlusion, where regions of an image are visible to one eye only (Harris & Wilcox, 2009;Nakayama & Shimojo, 1990;Tsirlin, Wilcox, & Allison, 2010). This absence of matching regions between left and right eyes results in changing patterns of disparity energy, where unmatched regions are likely to be associated with generally low levels of binocular correlation across a range of potential disparity values, and where the local image structure at these unmatched regions is more likely to match neighboring "background" image areas (Basgöze, White, Burge, & Cooper, 2020). Where binocular disparity information is available, disparitydefined boundaries are typically associated with large disparity gradients (Basgöze et al, 2020;Cammack & Harris, 2016;Goutcher et al, 2018;Goutcher & Wilcox, 2021).…”

“…This absence of matching regions between left and right eyes results in changing patterns of disparity energy, where unmatched regions are likely to be associated with generally low levels of binocular correlation across a range of potential disparity values, and where the local image structure at these unmatched regions is more likely to match neighboring "background" image areas (Basgöze, White, Burge, & Cooper, 2020). Where binocular disparity information is available, disparitydefined boundaries are typically associated with large disparity gradients (Basgöze et al, 2020;Cammack & Harris, 2016;Goutcher et al, 2018;Goutcher & Wilcox, 2021). Note that, for both half-occlusion and disparity-defined boundaries, binocular segmentation signals are the result of image-based, rather than depth or distance-based, computations.…”