权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Neural Basis of Shape from Texture

根据纹理确定形状的神经基础

基本信息

批准号：
8843856
负责人：
Qasim Zaidi
金额：
$ 35.47万
依托单位：
STATE COLLEGE OF OPTOMETRY
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-03-01 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8843856
关键词：
3-Dimensional Amblyopia Attention Bayesian Analysis Biological Brain Cognition Complex Computer Simulation Computer Vision Systems Conflict (Psychology)Contracts Convergence Insufficiency Cues Data Development Discrimination Eye Eye Movements Frequencies Geometry Head Movements Image Learning Light Measurement Measures Methods Modeling Motion Movement Neurologic Neurons Output Patients Pattern Perception Performance Process Property Psychophysics Relative (related person)Resolution Retina Retinal Rotation Shapes Signal Transduction Slide Space Perception Staging Stimulus Strabismus Stream Surface Swimming System Testing Texture Translating Variant Vision Visual Visual Perception Walking Work analog area MT area V1 base design movie nervous system disorder neural model novel object motion object shape prototype relating to nervous system research study response sample fixation

项目摘要

DESCRIPTION (provided by applicant): People often need to judge the shapes and movements of 3-D objects from a distance. Images on the retinae are 2-D, but pattern and motion information contain cues about 3-D shapes. Building on our previous work we expect to make significant progress in understanding 3-D shape perception from these cues, and thus offer a prototype for how the brain extracts information from the world to infer environmental properties. When surfaces have texture patterns, deformations of these patterns in retinal images provide clues for the brain to judge 3-D shape. When signals from the eyes reach the first cortical area V1, they are processed by neurons that are selectively tuned to orientations and spatial frequencies. We parsed texture deformations into orientation flows and spatial frequency gradients, to show that particular orientation flows evoke percepts of specific 3-D shapes, whereas frequency gradients provide cues to relative depth. These results led to models of how later cortical neurons could extract texture patterns and signal 3-D shapes. We now propose to extend our approach beyond static objects. When objects change shape (e.g. by bending, coiling, or contracting) as they move (e.g. walk, tumble, crawl, hop, slide, or swim), changes in the retinal image create patterns of local velocities that provide additional cues to 3-D shape. Since any retinal image can result from projections of many different 3-D objects, the brain relies on prior assumptions to infer the correct shape. Previous studies have only looked at rigid objects and used shape inference models based either on the brain assuming that the object is rigid or that faster points are nearer. We will use novel stimuli that put these prior assumptions in conflict, and thus examine how the brain potentiates a prior. This section will culminate in a model for choosing between conflicting assumptions, something that is often required in perception and cognition. Next, we will use randomly deforming non-rigid 3-D waves to examine how global shape properties, e.g. symmetry, influence perceived object motions by selectively combining disparate outputs of motion sensitive neurons. These results will unveil interactions between the form and motion cortical systems. Finally, we will examine observers' percepts of dynamic shape changes that require more sophisticated analyses of retinal velocity patterns. Neurons in the motion sensitive cortical area MT respond to 1-D motion shear and compression/divergence, so we will extract these qualities and combine them into 2-D velocity patterns of divergence, rotation, and deformation. Neural filters formed by these patterns will be used to explain perceived changes in 3-D shapes. We will base our filters on responses of MT and later cortical neurons to our stimuli, measured in a parallel project. We will thus present the first neural model that can explain observers' percepts of both rigid and non-rigid textured objects. The performance of our model will be compared against the best computer-vision models on motion-capture data from real deforming objects. We expect this project to introduce new ideas, methods and results for understanding visual perception of 3-D shapes and its deficits in neurological patients.

描述(申请人提供)：人们经常需要从远处判断三维物体的形状和运动。视网膜上的图像是2-D的，但图案和运动信息包含关于3-D形状的线索。在我们之前工作的基础上，我们希望在从这些线索中理解3D形状感知方面取得重大进展，从而为大脑如何从世界提取信息来推断环境属性提供一个原型。当表面有纹理图案时，视网膜图像中这些图案的变形为大脑判断三维形状提供了线索。当来自眼睛的信号到达第一个皮质区域V1时，它们被神经元处理，这些神经元被选择性地调整到方向和空间频率。我们将纹理变形解析为方向流和空间频率梯度，以表明特定的方向流唤起对特定3D形状的感知，而频率梯度提供相对深度的线索。这些结果导致了后来的大脑皮层神经元如何提取纹理模式和发出3D形状信号的模型。我们现在建议将我们的方法扩展到静态对象之外。当物体在移动(如行走、翻滚、爬行、跳跃、滑动或游泳)时改变形状(如弯曲、卷曲或收缩)时，视网膜图像的变化会产生局部速度的图案，这为3D形状提供了额外的线索。由于任何视网膜图像都可能来自许多不同的3D对象的投影，因此大脑依赖于先前的假设来推断正确的形状。以前的研究只观察了刚性物体，并使用了基于大脑的形状推理模型，这些模型要么假设物体是刚性的，要么假设速度较快的点离得更近。我们将使用新的刺激，使这些先前的假设发生冲突，从而检查大脑如何增强先前的假设。这一部分将在一个在相互冲突的假设之间进行选择的模型中达到高潮，这是感知和认知中经常需要的东西。接下来，我们将使用随机变形的非刚性3-D波来研究全局形状属性(例如对称性)如何通过选择性地组合运动敏感神经元的不同输出来影响感知的对象运动。这些结果将揭示形式和运动皮质系统之间的相互作用。最后，我们将检查观察者对动态形状变化的感知，这需要对视网膜速度模式进行更复杂的分析。运动敏感大脑皮层MT区的神经元对一维运动剪切和压缩/发散做出反应，所以我们将提取这些特征并将它们组合成发散、旋转和变形的二维速度模式。由这些模式形成的神经过滤器将被用来解释3D形状的感知变化。我们将根据MT和后来的皮质神经元对我们的刺激的反应来进行过滤，这是在一个平行的项目中测量的。因此，我们将向您介绍第一个神经模型，可以解释观察者对刚性和非刚性纹理对象的感知。我们的模型的性能将与最好的计算机视觉模型进行比较，这些模型基于来自真实变形对象的运动捕捉数据。我们期望这个项目能为神经科患者理解3D形状的视觉感知及其缺陷带来新的想法、方法和结果。