Active Depth Perception in Primates and Machines

尽管近年来计算能力有了极大的提高，但机器视觉系统仍然远远不能复制生物系统的效率、健壮性和速度。生物体和机器的一个关键区别在于获取视觉信息。与计算机不同，生物视觉系统不是被动地暴露在视觉场景中。相反，他们会通过目标导向的行为积极地寻找有用的信息。长期以来，人们一直认为，在行为和感知之间建立紧密联系，可能对开发更高效的机器视觉算法至关重要。在观察一个场景时，人类会协调眼睛的运动与头部和身体的微小运动。协调的头部/眼睛运动以视差的形式提供3D信息，视差是静止物体在不同距离的不同外观运动。为了检验这一行为在3D视觉中的影响，该项目将视觉皮质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)测量人类观察者头部/眼睛协调运动对深度和距离判断的准确性的影响；(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；以及(C)建立猕猴头部/眼睛运动引起的视差的提取和自主校准的模型。通过将大脑的神经模型与复制人类行为的机器人相结合，这项研究在人类和机器视觉研究之间建立了直接联系。它有可能为大脑提供新的见解，并为机器视觉中新算法的开发开辟道路。

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.