Visual Search and Detection in Natural Scenes

自然场景中的视觉搜索和检测

• 批准号: 10475662
• 负责人: WILSON S GEISLER
• 金额: $ 45.49万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475662
• 关键词: 3-Dimensional; Affect; Attention; Behavior; Behavioral; Data; Detection; Dimensions; Environment; Evolution; Goals; Human; Image; Individual; Laws; Lead; Learning; Literature; Location; Macaca; Masks; Measurement; Measures; Methods; Modeling; Monkeys; Noise; Performance; Physiological; Pilot Projects; Population; Positioning Attribute; Primates; Procedures; Property; Psychometrics; Publishing; Research; Retina; Sampling; Signal Detection Analysis; Signal Transduction; Stimulus; Testing; Thromboplastin; Training; Uncertainty; Variant; Vision; Visual; Visual impairment; Visual system structure; behavior measurement; computer studies; design; experimental analysis; ganglion cell; improved; luminance; neural circuit; neurophysiology; nonhuman primate; relating to nervous system; statistics; theories; tool; vision science; visual dysfunction; visual search

Project Summary/Abstract The broad aim is to understand the mechanisms that underlie the ability of human and non-human primates to locate and identify target objects under natural conditions, a ubiquitous and fundamental visual task. Localization and identification of objects under natural conditions is difficult because there are so many dimensions of uncertainty (variability) in natural tasks: highly-variable properties of the background scenes in which the target object may be embedded, uncertainty about possible 2D (or 3D) location of the target object in the scene, uncertainty about the 2D (or 3D) orientation of the target object, and uncertainty about the contrast of the target object. These natural dimensions of uncertainty drove the design of the visual system (though evolution and learning). Thus, the theoretical and experimental analysis of the effects of uncertainty is critical for understanding the neural computations performed by the visual system, as well as the underlying neural circuits. In preliminary studies, we made substantial progress in developing the theory of identification and localization under natural conditions, in developing relevant computational and neurophysiological tools, in making relevant behavioral measurements in humans, and in making relevant physiological measurements in behaving monkeys. The first aim will be directed at rigorously testing a number of hypotheses about how various dimensions of target and background uncertainty singly, and in combination, affect behavior and neural computation. The second aim will be directed at measuring and modeling an important factor (partial masking) that affects detection and identification performance in natural backgrounds, but has never been systematically investigated in either simple or natural backgrounds. The third aim builds on the first two aims and will be directed a developing and testing a new theory of overt and covert visual search that takes into account natural scene statistics, variation in ganglion cell sampling and RF size with retinal location, and variation in intrinsic position uncertainty with retinal location. The theory encompasses many specific models that each makes detailed quantitative and qualitative predictions for arbitrary specific search stimuli. Covert and overt search tasks in noise and natural backgrounds will be used to discriminate between models.

项目总结/摘要 广泛的目标是了解人类和非人类灵长类动物的能力背后的机制， 在自然条件下定位和识别目标物体，这是一项普遍存在的基本视觉任务。 在自然条件下的物体定位和识别是困难的，因为有这么多 自然任务中不确定性（可变性）的维度： 目标对象可以被嵌入的位置，关于目标对象在其中的可能的2D（或3D）位置的不确定性， 场景、关于目标对象的2D（或3D）取向的不确定性以及关于对比度的不确定性 的目标对象。这些不确定性的自然维度推动了视觉系统的设计（尽管 进化与学习）。因此，对不确定性影响的理论和实验分析是至关重要的 为了理解视觉系统执行的神经计算，以及底层的神经 电路.在初步研究中，我们在发展识别理论和 在自然条件下的定位，在开发相关的计算和神经生理学工具， 在人类中进行相关的行为测量，并在人类中进行相关的生理测量， 行为猴子第一个目标将是严格测试一些假设， 目标和背景不确定性的各个维度单独地和组合地影响行为和神经系统。 计算第二个目标将是针对测量和建模的一个重要因素（部分掩蔽） 影响自然背景中的检测和识别性能，但从未系统地 在简单或自然的背景下进行研究。第三个目标建立在前两个目标的基础上， 指导开发和测试一种新的显性和隐性视觉搜索理论， 场景统计，神经节细胞采样和RF大小随视网膜位置的变化，以及固有 视网膜位置的位置不确定性。该理论包含许多具体的模型，每一个模型都使 详细的定量和定性预测任意特定的搜索刺激。隐蔽搜查和公开搜查 噪声和自然背景中的任务将用于区分模型。