A Computational Theory of Motion Perception Modeling the Statistics of the Environment

本项目发展了视觉运动感知计算理论的新扩展。该理论的总体目标是理解人类如何在自然环境中感知运动；换句话说，当一个人看到鸟在飞翔，雪花在飘落，或者在自然视觉世界中出现的其他复杂的运动模式时，要了解他或她的大脑里发生了什么。在最近对一组有限的运动流模式的外观建模的基础上，本项目探索了一种基于贝叶斯理想观察者的概率方法，用于自然视觉的表示、学习和建模，以及使用学习到的概率模型来合成伪现实刺激。伪真实感刺激是一类新的视觉刺激，它具有自然视觉刺激的外观，但可以量化和以精确控制的方式变化。这种类型的刺激以前从未使用过，并提供了令人兴奋的前景，通过实验来理解视觉系统在暴露于现实但受控刺激时的行为。预计了解人类视觉系统如何处理运动将有助于开发更强大的计算机视觉算法，这些算法将具有许多技术应用。

This project develops a novel extension to a computational theory of visual motion perception. The overall goal of the theory is to understand how humans perceive motion in their natural environment; in other words, to understand what goes on inside a person's brain when he or she sees birds flying, snowflakes falling, or other complex patterns of motion that occur in the natural visual world. Building on recent work modeling the appearance of a limited set of motion flow patterns, the present project explores a probabilistic approach, based on Bayesian Ideal Observers, to the representation, learning, and modeling of natural visual, and the use of learned probabilistic models in turn to synthesize pseudo-realistic stimuli. Pseudo-realistic stimuli are a novel class of visual stimuli, which have the appearance of natural visual stimuli but can be quantified and varied in a precisely controlled manner. Stimuli of this type have never been used before and offer the exciting prospect of experimentally understanding the behavior of visual systems when exposed to realistic but controlled stimuli. It is anticipated that understanding how the human visual system processes motion will enable development of more robust and powerful computer vision algorithms which will have many technological applications.