Visual Search and Detection in Natural Scenes

自然场景中的视觉搜索和检测

• 批准号: 9331655
• 负责人: WILSON S GEISLER
• 金额: $ 38.57万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9331655
• 关键词: Anatomy; Area; Belief; Benchmarking; Biological; Brain; Complex; Computer Analysis; Detection; Development; Dimensions; Environment; Eye; Eye Movements; Functional disorder; Goals; Human; Ideal 1; Image; Lead; Location; Maps; Mathematics; Measurement; Measures; Membrane Potentials; Methods; Modeling; Monkeys; Noise; Occupations; Performance; Physiological; Physiology; Population; Primates; Probability; Procedures; Psychophysics; Publications; Reaction Time; Resolution; Retina; Sensory; Signal Transduction; Stimulus; Testing; Time; Training; Uncertainty; Vision; Visual; Visual Fields; Visual impairment; area striata; base; behavior measurement; computer studies; contrast imaging; design; experimental study; improved; insight; interest; neurophysiology; nonhuman primate; orientation columns; predictive modeling; public health relevance; relating to nervous system; response; sample fixation; statistics; vision science; visual search; voltage sensitive dye

 DESCRIPTION (provided by applicant): An ultimate goal of vision science is to understand and predict performance under natural conditions, and there is arguably no task more ubiquitous and fundamental than visually searching the environment for objects of interest. However, little is known about search in natural scenes. Our aim is to measure, characterize, and model search performance in natural scenes in both humans and non-human primates. We will study both covert search, where the search scene is presented for a limited time and the eyes remain on a fixation target, and overt search, which involves eye movements. Our approach combines mathematical/computational analysis, psychophysical and eye-movement measurements in humans and monkeys, and voltage sensitive dye imaging (VSDI) in V1 of behaving monkeys. We argue that a rigorous understanding of visual search must begin with a characterization of the sensory factors that control the detectability of the target across the visual field in absence of any uncertainty about the location of the target. In preliminary psychophysical, neurophysiological, and computational studies we developed a model of target detectability that predicts simple detection performance in natural images. In further computational studies (based on the detection results), we discovered a biologically-plausible computation that would allow the brain to perform optimal overt search in natural scenes. Specifically, we prove that during a visual search, the optimal next fixation location is obtained from the current belief map (posterior probability map) of where the target might be located, by (i) dividing (normalizing) the map by the local image contrast, (ii) blurring this contrast-normalized map appropriately, and then (iii) selecting the peak of the blurred map as the next fixation location. The proposed studies will measure human and monkey detection and visual search performance in natural images, and will measure VSDI responses in monkeys during fixation and detection tasks. The studies will let us test and further develop our models of simple detection and covert search in natural images, and determine which components of optimal fixation search humans use when searching in natural images. They may also lead to methods for training humans to use more optimal search strategies.

 描述（由申请人提供）：视觉科学的最终目标是理解和预测自然条件下的性能，并且可以说没有比视觉搜索环境中感兴趣的对象更普遍和基本的任务了。然而，人们对自然场景中的搜索知之甚少。我们的目标是测量，表征和模型搜索性能在自然场景中的人类和非人类灵长类动物。我们将研究隐蔽搜索，其中搜索场景在有限的时间内呈现，眼睛保持在固定目标上，以及涉及眼球运动的公开搜索。我们的方法结合了数学/计算分析，在人类和猴子的心理物理和眼动测量，和电压敏感染料成像（VSDI）在V1的行为猴子。我们认为，一个严格的理解视觉搜索必须开始的感官因素，控制目标的可检测性在整个视野中的目标的位置没有任何不确定性的表征。在初步的心理物理学，神经生理学和计算研究中，我们开发了一个目标可检测性模型，预测自然图像中的简单检测性能。在进一步的计算研究中（基于检测结果），我们发现了一种生物学上合理的计算，可以让大脑在自然场景中进行最佳的公开搜索。具体来说，我们证明了在视觉搜索过程中，最佳的下一个固定位置是从目标可能位于的当前置信图（后验概率图）中获得的，通过（i）将该图除以（归一化）局部图像对比度，（ii）适当地模糊该对比度归一化图，然后（iii）选择模糊图的峰值作为下一个固定位置。拟议的研究将测量人类和猴子在自然图像中的检测和视觉搜索性能，并将测量猴子在固定和检测任务期间的VSDI反应。这些研究将让我们测试并进一步发展我们的简单模型， 自然图像中的检测和隐蔽搜索，并确定人类在自然图像中搜索时使用的最佳固定搜索组件。它们还可能导致训练人类使用更优化的搜索策略的方法。