Active Depth Perception in Primates and Machines

灵长类动物和机器的主动深度感知

• 批准号: 0726901
• 负责人: Michele Rucci
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0726901&HistoricalAwards=false
• 关键词: Active; Depth; Perception; Primates; Machines

Despite the tremendous improvements in computational power of recent years, machine-vision systems are still far from replicating the efficiency, robustness, and speed of biological systems. A critical difference between organisms and machines lies in the acquisition of visual information. Unlike computers, biological vision systems are not passively exposed to the visual scene. Instead, they actively seek useful information by means of goal-directed behavior. It is a long-standing proposal that forging a tight link between behavior and perception may be critical for developing more efficient machine-vision algorithms. While examining a scene, humans coordinate eye movements with small movements of the head and body. Coordinated head/eye movements provide 3D information in the form of parallax, the different apparent motion of stationary objects at different distances. To examine the impact of this behavior in 3D vision, this project integrates computer modeling of the visual cortex with experiments in robotic vision and human psychophysics. The specific aims of this project are to: (a) measure the influence of coordinated head/eye movements on the accuracy of depth and distance judgments in human observers; (b) measure the 3D information resulting from head/eye movements by replicating human motor activity in an anthropomorphic robot; and (c) model the extraction and the autonomous calibration of the parallax resulting from head/eye movements in the parietal cortex of macaques. By coupling a neural model of the brain with a robot that replicates human behavior, this research establishes a direct link between human and machine vision studies. It has the potential of providing new insights on the brain as well as opening the way to the development of new algorithms in machine vision.

尽管近年来计算能力取得了巨大进步，但机器视觉系统仍远未复制生物系统的效率、稳健性和速度。 生物体和机器之间的一个关键区别在于视觉信息的获取。 与计算机不同，生物视觉系统不会被动地暴露于视觉场景。 相反，他们通过目标导向的行为积极寻找有用的信息。这是一个长期存在的提议，即在行为和感知之间建立紧密联系对于开发更高效的机器视觉算法可能至关重要。在检查场景时，人类会通过头部和身体的微小运动来协调眼球运动。 协调的头部/眼睛运动以视差的形式提供 3D 信息，即不同距离处静止物体的不同表观运动。 为了研究这种行为对 3D 视觉的影响，该项目将视觉皮层的计算机建模与机器人视觉和人类心理物理学的实验相结合。 该项目的具体目标是： (a) 测量协调的头部/眼睛运动对人类观察者深度和距离判断准确性的影响； (b) 通过在拟人机器人中复制人类运动活动来测量头部/眼睛运动产生的 3D 信息； (c) 对猕猴顶叶皮层中头部/眼睛运动产生的视差的提取和自主校准进行建模。 通过将大脑的神经模型与复制人类行为的机器人耦合起来，这项研究在人类和机器视觉研究之间建立了直接联系。它有潜力提供对大脑的新见解，并为机器视觉新算法的开发开辟道路。