Statistical and Neural Basis of Surface Inference in Vision

视觉表面推理的统计和神经基础

• 批准号: 0413211
• 负责人: Tai Sing Lee
• 金额: --
• 依托单位: Carnegie-Mellon 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: 2004-09-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413211&HistoricalAwards=false
• 关键词: Statistical; Neural; Basis; Surface; Inference

This is a multidisciplinary research program for investigating both the computational principles and the neural mechanisms underlying our visual perception of three-dimensional (3D) surfaces and shapes in the natural world. Its goal is to understand how surfaces of objects are inferred and represented in the brain. The general approach is first to discover the statistical regularities of patterns and structures in 3D natural scenes and to develop a computational framework for representing and inferring these structures from optical images; and second to test neurophysiologically the predictions generated by the computational framework on the neural basis of surface representation and inference. The fundamental hypothesis is that the visual system functions as a hierarchical probabilistic inference system in which the feedforward and feedback connections among the different visual areas in the cortical hierarchy serve to mediate two-way Bayesian belief propagation. In this framework, the brain is conjectured to actively construct a representation of the visual scene based on the retinal input as well as our prior knowledge and experience of the world. The investigator will carry out a novel statistical study of 3D natural scenes, develop efficient probabilistic computational algorithms for surface inference based on natural scene statistics, explore neural models for implementing such algorithms, and test neurophysiologically these models by recording and analyzing neuronal activity in the early visual areas of primate cerebral cortex. It is a tightly coupled interdisciplinary project that involves synergistic research in computer vision, computational neuroscience and systems neuroscience to address fundamental questions in these three fields. Understanding how the brain makes inference about the visual world will have a significant broad impact on neuroscience, clinical medicine and robotics. This integrated study of a hierarchical visual inference system and its associated probabilistic inference algorithms, rooted in natural scene statistics, will contribute to the foundation for building a new generation of flexible and intelligent robotic vision systems. Such systems will be able to learn and adapt to the statistical regularities of a changing environment and make inferences based on scene contexts. The proposed research program also provides an unique educational vehicle of interdisciplinary training to graduate and undergraduate students that will serve as a catalyst to integrate computer science research and biological research in the scientific community at large.

这是一个多学科的研究计划，用于研究我们对自然世界中三维（3D）表面和形状的视觉感知的计算原理和神经机制。 它的目标是了解物体的表面是如何在大脑中推断和表示的。一般的方法是首先发现在3D自然场景中的模式和结构的统计特征，并开发一个计算框架，用于表示和推断这些结构的光学图像;和第二，神经生理学测试的神经基础上的表面表示和推理的计算框架所产生的预测。基本假设是视觉系统作为一个分层的概率推理系统，其中皮层层次中不同视觉区域之间的前馈和反馈连接用于介导双向贝叶斯信念传播。在这个框架中，大脑被训练根据视网膜输入以及我们对世界的先验知识和经验来主动构建视觉场景的表示。 研究人员将对3D自然场景进行新的统计研究，开发基于自然场景统计的表面推理的有效概率计算算法，探索用于实现此类算法的神经模型，并通过记录和分析灵长类动物大脑皮层早期视觉区域的神经元活动来测试这些模型。这是一个紧密耦合的跨学科项目，涉及计算机视觉，计算神经科学和系统神经科学的协同研究，以解决这三个领域的基本问题。 了解大脑如何对视觉世界进行推理，将对神经科学、临床医学和机器人技术产生广泛的影响。这种基于自然场景统计的分层视觉推理系统及其相关概率推理算法的综合研究，将为构建新一代灵活智能的机器人视觉系统奠定基础。这样的系统将能够学习和适应不断变化的环境的统计数据，并根据场景上下文进行推断。拟议的研究计划还提供了一个独特的跨学科培训的教育工具，研究生和本科生，将作为催化剂，以整合计算机科学研究和生物学研究在科学界在大。