# Finding Simple and Camouflaged Objects in Noise and Natural Backgrounds

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $392,228

## Abstract

Project Summary/Abstract
In the previous project period we developed and tested principled theories for identification of
occluding targets and depth boundaries in noise and natural backgrounds (previous Aims 1 & 2).
The current aims are directed at extending these aims, and our previous work on visual search,
to the study of covert and overt visual search for additive and occluding targets. In preliminary
studies, we developed a principled theory of covert and overt search, and then tested the theory
in a covert search task with additive targets in simple noise backgrounds. We found that overall
search performance was near optimal, given the detectability across the visual field (the d' map)
of the human observers. However, surprisingly, detectability was substantially reduced in the
foveal region, but not in the periphery. In other words, there is substantial “foveal neglect” that
occurs when covert search is directed over an extended area. We propose to characterize and
test principled hypotheses for this phenomenon and its role in covert search both in simple and
natural backgrounds. In preliminary studies we also designed stimuli that isolate the role of the
boundary cues available for identification of occluding targets. In these stimuli, the target and
background have surface textures that are statistically the same. We will develop and test optimal
and suboptimal models of identification of these maximally-camouflaged targets, both for noise
and natural textures. Finally, we plan to measure and model overt search accuracy and eye
movements for additive and maximally-camouflaged occluding targets.

## Key facts

- **NIH application ID:** 10367497
- **Project number:** 2R01EY011747-23A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** WILSON S GEISLER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $392,228
- **Award type:** 2
- **Project period:** 1997-06-01 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367497

## Citation

> US National Institutes of Health, RePORTER application 10367497, Finding Simple and Camouflaged Objects in Noise and Natural Backgrounds (2R01EY011747-23A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10367497. Licensed CC0.

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