Computational and Neurophysiological Investigation of Robust Visual Inference

卡内基梅隆大学李大新的计算和神经生理学研究这个项目是一项多学科的研究，研究灵长类动物系统中稳健视觉推理的计算原理和神经机制，并利用这些原理来开发基于统计学的新的计算机视觉方法来推断视觉图像中的3D场景结构。真实3D场景的图像非常模糊，很难解释，因为它可能是由不同物理原因的许多可能组合产生的，例如光线、纹理和形状。计算机视觉中的经典方法试图通过用简化的假设对这些图像形成过程进行建模，然后对这些模型进行反转来进行3D场景推理。PI提出了一种统计方法，通过学习和利用自然环境中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信任传播算法，允许灵活地合并丰富的统计场景先验。这项计算工作将指导他利用先进的电生理技术研究灵长类早期视觉皮质中场景先验信息的神经编码和概率推理机制。更好地理解先验的神经表示和推理机制将代表着神经科学的基础科学进步，也将为改进基于统计的视觉推理的计算方法提供新的见解。

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.