RT Journal Article
SR Electronic
T1 The effect of depth variation on disparity tasks in natural scenes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 162222
DO 10.1101/162222
A1 Arvind Iyer
A1 Johannes Burge
YR 2017
UL http://biorxiv.org/content/early/2017/07/11/162222.abstract
AB Local depth variation is a distinctive property of natural scenes and its effects on perception have only recently begun to be investigated. Here, we demonstrate how natural depth variation impacts performance in two fundamental tasks related to stereopsis: half-occlusion detection and disparity detection. We report the results of a computational study that uses a large database of calibrated natural stereo-images with precisely co-registered laser-based distance measurements. First, we develop a procedure for precisely sampling stereo-image patches from the stereo-images, based on the distance measurements. The local depth variation in each stereo-image patch is quantified by disparity contrast. Next, we show that increased disparity contrast degrades performance in half-occlusion detection and disparity detection tasks, and changes the size and shape of the optimal spatial integration areas (“receptive fields”) for computing the task-relevant decision variables. Then, we show that a simple binocular image statistic predicts disparity contrast in natural scenes. Finally, we report results on the most likely patterns of disparity variation in natural scenes. Our findings motivate computational and psychophysical investigations of the mechanisms that underlie disparity estimation in local regions of natural scenes.