BIC: Eye movements and depth perception in primates and machines

我们的眼睛从不休息。即使我们是。在一个视觉目标上，微小的不自觉的眼球运动不断地干扰视网膜上图像的投影。最近的大量证据表明，这些。眼动是视觉信息在大脑中获取和表征的重要组成部分。对猴子的神经生理学研究表明，微小的眼球运动强烈地影响着大脑。观察视觉系统中神经元的反应。在之前获得美国国家科学基金会奖（EIA-0130851）的研究中，我们已经表明，注视不稳定性对于识别自然观看过程中出现的短暂刺激至关重要（Rucci和Desbordes，2003），并提高了外侧展状核和初级视觉皮层模型的视觉表征效率（Rucci和Casile，2003b,2004）。此外，机器人系统中人眼运动的再现也证明了这一点。国家不稳定性可以提供可靠的距离信息（Santini和Rucci，2003）。基于我们之前的研究结果和最近的神经生理学研究结果，本提案将继续NSF a和EIA-0130851的部分研究。它描述了一个研究项目，重点是提取和整合多个深度线索，以开发一个可靠的3D视觉场景表示。该项目采用跨学科的方法，将视觉皮层的计算机建模与机器人实验和非人类受试者的眼球运动测量相结合。这项研究的长期目标是开发机器视觉系统，通过模拟大脑的计算原理，实现高水平的鲁棒性和效率。该项目的具体目标是：1。根据最近的神经生理学研究，建立了猴子初级视觉皮层模型。结果表明，不同的神经元群体在di期间有反应。小眼球运动的事件阶段。(a)分析di传输的相对距离信息。当模型与复制人眼运动的机器人眼动工作站相结合时，事件神经元群；(b)对di提供的深度信息进行整合。将事件线索转换成连贯的3d视觉场景。通过学习研究模型的自组织，使深度信息的提取和集成能够根据系统的物理和运动特征自主调整。在这项研究中，人类观察者的眼球运动将由一个高分辨率的眼动仪测量，然后由一个机器人系统（一个带有2个移动摄像头的头/眼系统）精确地复制，该系统是我们专门设计的，用于在眼球运动行为中重现眼睛的视觉输入。在这种方法中获得的视觉图像将作为输入应用于灵长类动物纹状皮层神经元的计算模型。智力优势：该项目建立了人类和机器视觉研究之间的直接联系。通过关注眼球运动的计算机制。除了视觉处理之外，它有可能提供关于大脑的新见解，并为机器视觉中新算法的发展开辟道路。更广泛的影响：本研究的跨学科性质。为培训学生提供了新的机会。通过与PI合作，波士顿大学认知和神经系统系的学生将有机会将他们的理论工作与机器视觉系统的发展结合起来。波士顿大学认知和神经系统系的重点是大脑的计算建模。

Our eyes are never at rest.Even when e are .xating upon a visual target,small involuntary eyemovements continuously perturb the projection of the image on the retina.A substantial body ofrecent evidence indicates that these .xational eye movements are an important component of theay visual information is acquired and represented in the brain.Neurophysiological studies withmonkeys have sho n that small eye movements strongly a .ect the responses of neurons in the visualsystem.In the research of a previous NSF award (EIA-0130851),e have shown that fixationalinstability is crucial for identifying stimuli presented for the brief durations that occur duringnatural viewing (Rucci and Desbordes,2003),and improves the efficiency of visual representationsin models of the lateral geniculate nucleus and primary visual cortex (Rucci and Casile,2003b,2004).Furthermore,reproduction of human eye movements in a robotic system has sho n that.xational instability may provide reliable information of distance (Santini and Rucci,2003).Building on our previous results and those of recent neurophysiological studies,this proposalcontinues part of the research of NSF a ard EIA-0130851.It describes a program of research thatfocuses on extracting and integrating multiple depth cues to develop a reliable 3D representationof the visual scene.This project follows an interdisciplinary approach that integrates computermodeling of the visual cortex with robotic experiments and measurements of eye movements inhuman subjects.The long-term goal of this research is to develop machine vision systems that,byemulating the computational principles of the brain,achieve high levels of robustness and e .ciency.The specific aims of this project are to:1.Develop a model of the monkey primary visual cortex in which,following recent neurophysi-ological .ndings,distinct populations of neurons respond during di .erent phases of small eyemovements.2.(a)Analyze the information of relative distance transmitted by di .erent neuronal populationswhen the model is coupled with a robotic oculomotor orkstation that replicates human eyemovements;and (b)integrate depth information provided by di .erent cues into a coherent3D representation of the visual scene.3.Investigate the self-organization of the model by means of learning,so that the extractionand integration of depth information is autonomously tuned to the physical and motor char-acteristics of the system.In this research,the eye movements of human observers will be measured by a high-resolutioneye-tracker,and then accurately replicated by a robotic system (a head/eye system with t o mo-bile cameras)that e have speci .cally designed to reproduce the visual inputs to the eyes duringoculomotor behavior.Visual images acquired in this ay will be applied as input to computationalmodels of neurons in the striate cortex of primates.Intellectual merit: This project establishesa direct linkbet een human and machine vision studies.By focusing on the computational mecha-nisms by which eye movements a .ect visual processing,it has the potential of providing new insightson the brain as ell as opening the ay to the development of new algorithms in machine vision.Broader impact: The interdisciplinary nature of this research o .ers new opportunities for train-ing students.By collaborating with the PI,students in the Department of Cognitive and NeuralSystems at Boston University,a department ith an important focus on computational modelingof the brain,will have an opportunity to combine their theoretical orkwith the development ofmachine vision systems.