Computational and Neurophysiological Investigation of Robust Visual Inference

鲁棒视觉推理的计算和神经生理学研究

• 批准号: 0713206
• 负责人: Tai Sing Lee
• 金额: $ 44.97万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713206&HistoricalAwards=false
• 关键词: Computational; Neurophysiological; Investigation; Robust; Visual

Computational and neurophysiological investigation of robust visual inference Tai Sing Lee, Carnegie Mellon University This project is a multi-disciplinary investigation of the computational principles and neural mechanisms underlying robust visual inference in primate systems and the exploitation of these principles to develop new statistics-based computer vision approaches for inferring 3D scene structures in visual images. An image of a real 3D scene is highly ambiguous and difficult to interpret because it could be generated by many possible combinations of the different physical causes, such as lighting, texture and shapes. Classical approaches in computer vision attempt 3D scene inference by modeling these image formation processes with simplified assumptions and then inverting these models. The PI proposes a statistical approach to better solve these problems by learning and exploiting the statistical priors on 3D shapes in the natural environment and their correlational structures with 2D images. The PI plans to develop efficient Bayesian belief propagation algorithms within the framework of probabilistic graphical models that allow flexible incorporation of rich statistical scene priors. The computational work will guide his investigation of the neural encoding of scene priors and the mechanisms of probabilistic inference in the primate early visual cortex using advanced electrophysiological techniques. A better understanding of the neural representations of priors and mechanisms of inference will represent a fundamental scientific advance in neuroscience and will also provide new insights for improving the statistics-based computational approaches for visual inference.

鲁棒视觉推理的计算和神经生理学研究李大星，卡内基梅隆大学这个项目是一个多学科的研究，研究灵长类系统中鲁棒视觉推理的计算原理和神经机制，并利用这些原理开发新的基于神经网络的计算机视觉方法，用于推断视觉图像中的3D场景结构。真实的3D场景的图像是高度模糊的，并且难以解释，因为它可以由不同物理原因（诸如照明、纹理和形状）的许多可能组合生成。计算机视觉中的经典方法尝试通过用简化的假设对这些图像形成过程进行建模，然后反转这些模型来进行3D场景推断。PI提出了一种统计方法，通过学习和利用自然环境中的3D形状及其与2D图像的相关结构的统计先验，来更好地解决这些问题。PI计划在概率图形模型的框架内开发高效的贝叶斯置信传播算法，该模型允许灵活地结合丰富的统计场景先验。计算工作将指导他的调查的神经编码的场景先验和概率推理的机制，在灵长类动物早期视觉皮层使用先进的电生理技术。更好地理解先验的神经表征和推理机制将代表神经科学的基本科学进步，也将为改进基于神经学的视觉推理计算方法提供新的见解。