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Estimation and Discrimination of Motion and Depth in Natural Scenes

自然场景中运动和深度的估计和判别

基本信息

批准号：
10391490
负责人：
Johannes D. Burge
金额：
$ 37.19万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10391490
关键词：
Animals Basic Science Behavior Behavioral Biological Communities Cues Data Databases Depth Perception Discrimination Environment Eye Future Goals Human Image Investigation Knowledge Laboratories Left Literature Location Measurement Measures Modeling Motion Motion Perception Noise Organism Output Perception Performance Probability Theory Process Property Psychology Psychophysics Research Retina Series Services Source Specific qualifier value Speed Step Tests Stimulus System Task Performances Techniques Testing Time Uncertainty Vision Vision Disparity Vision research Visual Visual system structure Work base computerized tools experimental study image processing improved movie neurophysiology object perception predictive modeling programs receptive field retinal imaging statistics theories three dimensional structure tool vision science visual information visual neuroscience visual processing

项目摘要

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. !

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。这些研究的结果将不仅是独特的新的测量，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。 !