# Estimation and Discrimination of Motion and Depth in Natural Scenes

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $383,425

## Abstract

Project Summary/Abstract
A fundamental goal of vision research is to understand how vision works in natural conditions. Vision systems
are matched to the critical tasks that organisms perform to survive and reproduce. Thus, it is fundamentally
important to analyze vision systems with respect to these tasks, and the properties of natural stimuli that are
relevant to those tasks. My lab takes the following approach. First, we measure task-relevant statistical
properties of natural stimuli. Next, given biological constraints, we determine how to optimally use those
properties to perform the tasks. Then, we formulate hypotheses based on the first two steps and test them in
behavioral experiments with natural stimuli. To connect our results with the classic literature and determine the
generality of our results, we also collect data with artificial stimuli. Using a unique suite of natural image
databases, computational tools, and psychophysical paradigms (many of which have been developed or
refined in our laboratory), we propose to investigate several fundamental tasks relevant for the estimation of
depth and motion in natural scenes. Aim 1 investigates optimal and human disparity estimation in natural
stereo-images. Aim 2 investigates optimal and human motion estimation in natural image movies. Aim 3
investigates optimal and human motion-in-depth estimation in natural stereo-image movies. Many of the
proposed studies will be the first to characterize the statistical properties of natural images that underlie the
human ability to perform these tasks accurately. Many of the proposed studies will also be the first to measure
human performance in these tasks using natural stimuli. The result of these studies will be not only unique new
measurements, but new principled models that can predict human performance under natural conditions and
guide future behavioral and neurophysiological studies of the underlying mechanisms. Encouraging preliminary
results have been obtained for many of the proposed studies.
!

## Key facts

- **NIH application ID:** 9928934
- **Project number:** 5R01EY028571-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Johannes D. Burge
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $383,425
- **Award type:** 5
- **Project period:** 2018-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928934

## Citation

> US National Institutes of Health, RePORTER application 9928934, Estimation and Discrimination of Motion and Depth in Natural Scenes (5R01EY028571-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9928934. Licensed CC0.

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