Computational Theory of Motion Perception

运动感知的计算理论

• 批准号: 0613563
• 负责人: Alan Yuille
• 金额: $ 40.6万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0613563&HistoricalAwards=false
• 关键词: Computational; Theory; Motion; Perception

This proposal will develop fundamental understanding of aspects of the human visual system. The goal is to understand how humans perceive motion . In other words, to understand what goes on inside a human's brain when he, or she, looks at a group of birds flying or snowflakes falling. The proposal is interdisciplinary and combines computational theory, psychophysics and physiology. The computational theory provides a mathematical model for how humans process motion. The theory is implemented by computer algorithms, which are applied to a motion sequence of images, and which predict the estimated velocities of motion and other properties. These motion sequences of images are pseudo-realistic, in the sense that they appear to be natural images (e.g. of flying birds, or snow) but instead are artificial parameterized models (which enables us to design controlled experiments by altering the parameters). The psychophysical experiments compare the predictions of our theory with the performance of human subjects performing a range of motion estimation tasks. These experiments address issues such as, what types of motion stimuli are people expert at perceiving? The physiological experiments attempt to pin down where motion processing takes place in the brain. In particular, we study the activity of neural cells (neurons) in different parts of the brain during motion perception. It is anticipated that understanding how the human visual system processes motion will enable us to develop more robust and powerful computer vision algorithms which will have many technological applications (e.g. for robotics and automated medical diagnosis). In addition, understanding how neurons perform computations is central to the entire enterprise of neuroscience in its attempt to give a scientific account of the brain mechanisms underlying our mental life.The grant will help encourage underrepresented groups by supporting a female postdoctoral researcher. The grant will include data sharing of the multi-electrode recordings of the physiological experiments and, in addition, we will make available the code for making novel pseudo-random stimuli. The proposal also has educational impact because it will help train a graduate student in interdisciplinary research, encompassing computer science, psychology, neuroscience and statistics.

该提案将发展对人类视觉系统各个方面的基本理解。我们的目标是了解人类如何感知运动。换句话说，就是要了解当一个人看到一群飞翔的鸟儿或飘落的雪花时，他或她的大脑里发生了什么。该提案是跨学科的，结合了计算理论，心理物理学和生理学。计算理论为人类如何处理运动提供了数学模型。该理论通过计算机算法来实现，该算法被应用于图像的运动序列，并且预测估计的运动速度和其他属性。这些图像的运动序列是伪现实的，从某种意义上说，它们看起来是自然图像（例如飞鸟或雪），但实际上是人工参数化模型（这使我们能够通过改变参数来设计受控实验）。 心理物理实验比较了我们的理论预测与人类受试者执行一系列运动估计任务的表现。这些实验解决的问题，如，什么类型的运动刺激是人们的专家在感知？生理学实验试图确定大脑中运动处理发生的位置。特别是，我们研究在运动感知过程中大脑不同部位的神经细胞（神经元）的活动。 预计了解人类视觉系统如何处理运动将使我们能够开发出更强大和更强大的计算机视觉算法，这些算法将具有许多技术应用（例如机器人和自动化医疗诊断）。 此外，了解神经元如何执行计算是整个神经科学事业的核心，它试图对我们精神生活背后的大脑机制进行科学解释。该基金将通过支持一位女性博士后研究人员，帮助鼓励代表性不足的群体。 该补助金将包括生理实验的多电极记录的数据共享，此外，我们将提供用于制作新的伪随机刺激的代码。 该提案还具有教育影响，因为它将有助于培养跨学科研究的研究生，包括计算机科学，心理学，神经科学和统计学。