ITR: Foundations of Visual Search

项目摘要这项研究旨在开发灵活的、通用的视觉搜索系统，能够在真实的、杂乱的环境中搜索对象。这项研究将包括在人类和灵长类动物身上进行广泛的心理物理和生理实验，以制作模仿这种行为的人工系统的原型。这项研究的目标可以方便地分为四个目标：目标1：开发和原型一种革命性的相机凝视控制设备，称为远程高速主动视觉环境，或RHAVEN。RHAVEN将允许远程呈现使用眼睛运动控制远程摄像机的凝视，就像直接观看场景一样。目标2：通过将视觉搜索描述为贝叶斯问题，开发视觉搜索的最佳统计界限，产生最大后验(MAP)解决方案，首先，使用最少的注视次数在视觉场景中寻找目标，其次，对于给定当前注视的下一注视选择。目标3：基于凝视点的自然场景统计构建视觉搜索的模型。通过分析眼动和注视采样的自然场景的统计数据，可以推断出视觉上重要的图像结构。目标4：对视觉搜索任务中清醒的、行为灵长类动物进行神经生理学研究。测量和分析清醒的猴子的搜索性能，同时测量大脑前部眼区(FEF)中有助于控制眼跳运动的神经元群的反应。广泛影响：这项研究的结果将显著影响许多国家的优先事项：搜索大型视觉数据库、机器人导航、安全成像、生物医学搜索、视觉神经科学等。从视觉搜索的基本理论中受益的场景是很容易想象的。例如：在机场安检系统中搜索可疑人脸；检查互联网数据流中的可疑材料；半自动搜索乳房X光检查中的病变；驾驶机器人车辆绕过恶劣环境中的障碍物；导航巨大的可视数据库，等等。

Project AbstractThis study is directed towards developing flexible, general-purpose Visual Search systems capable of Searching for objects in real, cluttered environments. The research will include extensive psychophysical and physiological experiments on humans and primates that will prototype artificial systems that mimic this behavior. The goals of the study can be conveniently divided into four Aims:Aim 1: Develop and prototype a revolutionary camera gaze control device dubbed Remote High-Speed Active Visual Environment, or RHAVEN. RHAVEN will allow telepresent control of the gaze of a remote camera using eye movements as rapidly and naturally as if viewing the scene directly.Aim 2: Develop optimal statistical bounds on Visual Search, by casting it as a Bayesian problem, yielding a maximum a posteriori (MAP) solutions for firstly, finding a target in a visual scene using a smallest number of fixations, and secondly, for next-fixation selection given a current fixation.Aim 3: Construct models for Visual Search based on Natural Scene Statistics at the point of gaze. Visually important image structures can be inferred by analyzing the statistics of natural scenes sampled by eye movements and fixations.Aim 4: Conduct neurophysiological studies on awake, behaving primates during Visual Search tasks. Measure and analyze search performance in awake, behaving monkeys, while measuring the responses of neural populations in the brain's frontal eye fields (FEF) which help control saccadic eye movements.Broader Impact: The results of this research should significantly impact numerous National Priorities: Searching Large Visual Databases, Robotic Navigation, Security Imaging, Biomedical Search, Visual Neuroscience, and many others. It is easy to envision scenarios that would benefit by a fundamental theory of Visual Search. For example: searching for suspect faces in airport security systems; examining internet streams for questionable material; semi-automatic search for lesions in mammograms; steering robotic vehicles around obstacles in hostile environs; navigating huge visual data libraries, etc.