BIC: Eye movements and depth perception in primates and machines

BIC：灵长类动物和机器的眼球运动和深度知觉

• 批准号: 0432104
• 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: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0432104&HistoricalAwards=false
• 关键词: BIC; Eye; movements; depth; perception

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.

我们的眼睛永远不会休息。即使在注视视觉目标时，微小的不自觉的眼球运动也会不断扰乱图像在视网膜上的投影。最近的大量证据表明，这些注视性眼球运动是大脑获取和表征视觉信息的重要组成部分。对猴子的神经生理学研究表明，微小的眼球运动强烈影响大脑中神经元的反应。 在之前的 NSF 奖项 (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期间发生的短暂持续时间的刺激至关重要（Rucci 和 Desbordes，2003），并提高了外侧膝状体核和初级视觉皮层模型中视觉表征的效率（Rucci 和 Casile，2003b，2004）。此外，机器人系统中人眼运动的再现表明。轴系不稳定性可以提供可靠的距离信息（Santini 和 Rucci，2003）。基于我们之前的结果和最近的神经生理学研究，该提案继续了 NSF 和 EIA-0130851 研究的一部分。它描述了一个研究计划 专注于提取和集成多个深度线索，以开发视觉场景的可靠 3D 表示。该项目采用跨学科方法，将视觉皮层的计算机建模与机器人实验和人类受试者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。具体 该项目的目标是：1.开发一个猴子初级视觉皮层模型，根据最近的神经生理学发现，不同的神经元群体在小眼球运动的不同阶段做出反应。2.(a)当该模型与复制人类的机器人动眼神经工作台结合时，分析不同神经元群体传输的相对距离信息 眼球运动；(b)将不同线索提供的深度信息整合成视觉场景的连贯3D表示。3.通过学习研究模型的自组织，使深度信息的提取和整合自主调整到系统的物理和运动特征。在这项研究中，人类观察者的眼球运动将通过高分辨率眼动追踪器进行测量，并且 然后由专门设计用于在动眼行为期间再现眼睛视觉输入的机器人系统（带有移动摄像头的头/眼系统）精确复制。由此获得的视觉图像将用作灵长类动物纹状皮层神经元计算模型的输入。智力优点：该项目在人类和机器视觉之间建立了直接联系 通过关注眼球运动影响视觉处理的计算机制，它有可能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。更广泛的影响：这项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， 重点关注大脑计算建模的部门将有机会将其理论工作与机器视觉系统的开发相结合。