The power of the human visual system derives in large part from the ability of its perceptual grouping mechanisms to find structure and organization in the images encoded by the retinas. We propose to continue and extend our general quantitative approach to understanding the mechanisms of perceptual grouping. Our approach consists of: (1) measuring the statistical properties of natural images that are relevant for perceptual grouping, (2) developing models of perceptual grouping that are informed by the statistical properties of natural images, by the physiology and psychophysics of low-level vision, and by computational principles, and (3) testing the predictions of these models and competing models in human psychophysical experiments. We propose to measure natural scene statistics for image contours and regions using two methods. One involves measuring local features in images and then computing simple co- occurrence statistics, i.e., the joint probabilities of different possible feature values. The other involves hand segmenting images, measuring local features in the images, and then computing co-occurrence statistics that are conditional on the segmentation. These statistics, especially the conditional statistics, will be used to generate Bayesian models for contour and region grouping. In preliminary studies, we discovered a number of robust statistical properties of natural images and have begun developing models of perceptual grouping where those properties are exploited in an optimal (rational) fashion. A number of contour grouping and region grouping experiments are proposed to test these models. We have made good progress with this general approach during previous project period. This work is important because understanding the physics of natural stimuli is essential for understanding natural (real) perceptual tasks and just about every design aspect of the visual system, and because perceptual grouping mechanisms play a central role in all higher order visual functions including form, shape, object and scene perception. Of note are studies that will provide the first detailed measurements of contour and region statistics of close-up foliage, which is of particular relevance for studies of visual mechanisms in the monkey. Also of note are studies of the role of peripheral vision and eye movements in natural tasks. Understanding these capabilities is crucial for understanding the effects of low vision on visual performance and how best to mitigate those effects.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Central Visual Processing Study Section (CVP)
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Wiggs, Cheri
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University of Texas Austin
Schools of Arts and Sciences
United States
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Sebastian, Stephen; Abrams, Jared; Geisler, Wilson S (2017) Constrained sampling experiments reveal principles of detection in natural scenes. Proc Natl Acad Sci U S A 114:E5731-E5740
Burge, Johannes; McCann, Brian C; Geisler, Wilson S (2016) Estimating 3D tilt from local image cues in natural scenes. J Vis 16:2
Paulun, Vivian C; Sch├╝tz, Alexander C; Michel, Melchi M et al. (2015) Visual search under scotopic lighting conditions. Vision Res 113:155-68
Burge, Johannes; Geisler, Wilson S (2015) Optimal speed estimation in natural image movies predicts human performance. Nat Commun 6:7900
Sebastian, Stephen; Burge, Johannes; Geisler, Wilson S (2015) Defocus blur discrimination in natural images with natural optics. J Vis 15:16
Bradley, Chris; Abrams, Jared; Geisler, Wilson S (2014) Retina-V1 model of detectability across the visual field. J Vis 14:
Burge, Johannes; Geisler, Wilson S (2014) Optimal disparity estimation in natural stereo images. J Vis 14:
Morgenstern, Yaniv; Geisler, Wilson S; Murray, Richard F (2014) Human vision is attuned to the diffuseness of natural light. J Vis 14:
Michel, Melchi M; Chen, Yuzhi; Geisler, Wilson S et al. (2013) An illusion predicted by V1 population activity implicates cortical topography in shape perception. Nat Neurosci 16:1477-83
D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S (2013) Humans make efficient use of natural image statistics when performing spatial interpolation. J Vis 13:

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