Estimation and Discrimination of Motion and Depth in Natural Scenes
Estimation and Discrimination of Motion and Depth in Natural Scenes
批准号:
10391490
负责人:
Johannes D. Burge
金额:
$37.19万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-04-01 至 2024-03-31
关键词:
AnimalsBasic ScienceBehaviorBehavioralBiologicalCommunitiesCuesDataDatabasesDepth PerceptionDiscriminationEnvironmentEyeFutureGoalsHumanImageInvestigationKnowledgeLaboratoriesLeftLiteratureLocationMeasurementMeasuresModelingMotionMotion PerceptionNoiseOrganismOutputPerceptionPerformanceProbability TheoryProcessPropertyPsychologyPsychophysicsResearchRetinaSeriesServicesSourceSpecific qualifier valueSpeedStep TestsStimulusSystemTask PerformancesTechniquesTestingTimeUncertaintyVisionVision DisparityVision researchVisualVisual system structureWorkbasecomputerized toolsexperimental studyimage processingimprovedmovieneurophysiologyobject perceptionpredictive modelingprogramsreceptive fieldretinal imagingstatisticstheoriesthree dimensional structuretoolvision sciencevisual informationvisual neurosciencevisual processing
中文摘要
项目总结/文摘
英文摘要
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.
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期刊论文(7)
专著(0)
科研奖励(0)
会议论文
Shape, perspective, and what is and is not perceived: Comment on Morales, Bax, and Firestone (2020).
形状、视角以及感知到的和不感知到的东西:对 Morales、Bax 和 Firestone 的评论 (2020)。
DOI:
10.1037/rev0000363
发表时间:
2023
期刊:
Psychological review
影响因子:
5.4
作者:
[Burge,Johannes, Burge,Tyler]
通讯作者:
Burge,Tyler
The statistics of how natural images drive the responses of neurons.
关于自然图像如何驱动神经元反应的统计数据。
DOI:
10.1167/19.13.4
发表时间:
2019
期刊:
Journal of vision
影响因子:
1.8
作者:
[Iyer,Arvind, Burge,Johannes]
通讯作者:
Burge,Johannes
DOI:
10.1167/jov.22.12.12
发表时间:
2022-11-01
期刊:
Journal of vision
影响因子:
1.8
作者:
[]
通讯作者:
海外基金