Detection and Estimation of Local Properties in Natural Scenes

NIH RePORTER · NIH · R01 · $388,300 · view on reporter.nih.gov ↗

Abstract

Project Summary/Abstract Visual systems must be matched (via evolution and learning over the lifespan) to the natural tasks organisms perform to survive and reproduce. Thus, it is of fundamental importance to analyze visual systems with respect to natural tasks and with respect to the statistical properties of natural stimuli relevant to performing those tasks. In our lab we call this “natural systems analysis.” This novel approach to vision science is composed of several steps: (1) identify natural tasks, (2) measure the natural scene statistics relevant for those tasks, (3) determine how to optimally use those statistics to perform the tasks, given appropriate biological constraints, and (4) use the first three steps to formulate principled hypotheses which are tested and refined in behavioral or physiological experiments. Using a unique suite of measurement devices, computational tools, and psychophysical paradigms developed in our laboratory, we propose to tackle (within the framework of natural systems analysis) several fundamental tasks involving estimation of local properties in natural scenes: (Aim 1) detection of occluding and partially-occluded targets in natural images, (Aim 2) detection of depth edges created by occluding surfaces and estimation local 3D surface orientation at the non-depth edge locations within those surfaces, and (Aim 3) estimation of disparity and local 2D motion. Many of the proposed studies will be the first to precisely characterize the statistical constraints in natural images underlying the visual system's ability to perform these tasks accurately. Many of the proposed studies will also be the first to measure performance in these fundamental tasks using natural stimuli. The product of the studies will be not only unique new measurements, but principled new models that can predict human performance under natural conditions and guide future neurophysiological studies of the underlying mechanisms. Strong preliminary results have been obtained in the previous project period for many of the proposed studies.

Key facts

NIH application ID
9831561
Project number
5R01EY011747-22
Recipient
UNIVERSITY OF TEXAS AT AUSTIN
Principal Investigator
WILSON S GEISLER
Activity code
R01
Funding institute
NIH
Fiscal year
2020
Award amount
$388,300
Award type
5
Project period
1997-06-01 → 2021-11-30