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/摘要
视觉研究的一个基本目标是了解视觉在自然条件下是如何工作的。视觉系统
与生物体生存和繁殖的关键任务相匹配。因此,从根本上说,
重要的是分析视觉系统相对于这些任务,和自然刺激的属性,
与这些任务有关。我的实验室采取了以下方法。首先,我们测量与任务相关的统计数据,
自然刺激的特性。接下来,考虑到生物学上的限制,我们决定如何最佳地利用这些限制,
属性来执行任务。然后,我们根据前两步制定假设,并在
自然刺激的行为实验。将我们的结果与经典文献联系起来,
我们的结果的一般性,我们也收集数据与人工刺激。使用一套独特的自然图像
数据库、计算工具和心理物理学范式(其中许多已经开发或
在我们的实验室完善),我们建议调查几个基本任务相关的估计
自然场景中的深度和运动。目的1研究自然环境中的最优视差估计和人类视差估计
立体影像目的2研究自然图像电影中的最佳和人体运动估计。目标3
研究自然立体图像电影中的最佳和人体运动深度估计。许多
拟议的研究将是第一个表征自然图像的统计特性,这些特性是自然图像的基础。
人类准确执行这些任务的能力。许多拟议的研究也将是第一次衡量
人类在这些任务中使用自然刺激的表现。这些研究的结果不仅是独特的新成果
测量,但新的原则模型,可以预测人类在自然条件下的表现,
指导未来的行为和神经生理学研究的潜在机制。令人鼓舞的初步
许多拟议的研究已经取得了成果。
!
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.
!
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