# Detection and Estimation of Local Properties in Natural Scenes

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $388,300

## 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 organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** WILSON S GEISLER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $388,300
- **Award type:** 5
- **Project period:** 1997-06-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9831561

## Citation

> US National Institutes of Health, RePORTER application 9831561, Detection and Estimation of Local Properties in Natural Scenes (5R01EY011747-22). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9831561. Licensed CC0.

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