CAREER: Generalized Separation of Style and Content on Nonlinear Manifolds with Application to Human Motion Analysis

职业：非线性流形上风格和内容的广义分离及其在人体运动分析中的应用

• 批准号: 0546372
• 负责人: Ahmed Elgammal
• 金额: $ 50.02万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546372&HistoricalAwards=false
• 关键词: CAREER; Generalized; Separation; Style; Content

Title: CAREER: Generalized Separation of Style and Content on Nonlinear Manifolds with Application to Human Motion AnalysisThe visual input is a function of various conceptually orthogonal factors. Each of these factors, typically, can be represented as an underlying nonlinear manifold. So, in general, each data point lies on a mixture of manifolds. Therefore, we have a product space of all these factors, which makes the problem very challenging. However, the problem can be approached if we understand conceptually, to some extent, the topology, dimensionality and the properties of each individual manifold of the orthogonal factors that generated the data. The ultimate goal of this research is to establish general mathematical frameworks for the separation of multiple factors in the data. In particular, in context of human motion, the objective is to establish a mathematical framework that decouples intrinsic body configuration from other sources of variability that affect the visual input and, consequently, to exploit such models in recovering body configuration. To achieve this goal four research directions will be investigated 1) Learning a unified invariant content manifold representation from various style variations on the same manifold. 2) Learning factorized generative models for the data given representation of one or more of the underlying manifolds. 3) Given representation of the underlying manifold, how that can be used to select discriminative features in the visual input. 4) Applying the findings towards the recovery of intrinsic body configuration.The problem of separation of style and content is an essential task in visual perception and is a fundamental mystery of perception. It is not clear how we perceive a common motion, such as walking, regardless of all sources of variations in its appearance. The fundamental research problems addressed in this research plan appear extensively in different computer vision as well as machine learning applications. The findings will help promote the state-of-the-art in computer vision and machine learning fields as well as bringing interesting computational models to researchers in the cognitive science field. Human motion analysis will be the main applied domain for this research. The proposed research in human motion analysis has various important applications such as surveillance, security, human computer interaction, etc. Human motion analysis will be the integrating theme between the research and the educational activities for motivating Math and Science education. The educational plan consists of several integrated activities targeting the graduate level, the undergraduate level, and high school educators and students. The goal is to develop educational tools that will integrate the efforts of the PI, high school educators, undergraduate and high school students through collaborating in the design, implementation, and evaluation of a computer vision virtual classroom.URL: http://www.cs.rutgers.edu/~elgammal/Research/GStyleContent.htm

标题：职业：随着人类运动分析的应用，在非线性歧管上的样式和内容的广义分离，视觉输入是各种概念上正交因素的函数。通常，这些因素中的每一个都可以表示为潜在的非线性歧管。因此，通常，每个数据点都在歧管的混合物上。因此，我们有所有这些因素的产品空间，这使问题变得非常具有挑战性。但是，如果我们在某种程度上从概念上理解拓扑，维度和生成数据正交因素的每个单独的属性的拓扑，维度和特性，就可以解决问题。这项研究的最终目标是建立一般数学框架，以分离数据中的多个因素。特别是，在人类运动的背景下，目标是建立一个数学框架，该框架将固有的身体配置与影响视觉输入的其他可变性来源，从而将这种模型利用在恢复身体配置时利用。为了实现这一目标，将研究四个研究方向1）从同一歧管上的各种样式变化中学习统一的不变内容歧管表示。 2）学习分解的生成模型，用于给出一个或多个基础歧管的给定表示。 3）给定代表下面的歧管，如何使用视觉输入中选择区分特征。 4）将发现应用于内在的身体配置的恢复。样式和内容分离的问题是视觉感知中的重要任务，并且是感知的基本谜团。 尚不清楚我们如何看待一个共同的运动，例如步行，无论其外观各不相同。本研究计划中解决的基本研究问题广泛出现在不同的计算机视觉以及机器学习应用中。这些发现将有助于促进计算机视觉和机器学习领域的最先进，并将有趣的计算模型带给认知科学领域的研究人员。人类运动分析将是这项研究的主要应用领域。人类运动分析中提出的研究具有各种重要应用，例如监视，安全性，人类计算机互动等。人类运动分析将是研究与激励数学和科学教育的教育活动之间的综合主题。该教育计划包括针对研究生水平，本科水平以及高中教育者和学生的几项综合活动。目的是开发教育工具，以通过在设计，实施和评估计算机视觉virtual classroom.url的设计，实施和评估中进行PI，高中教育者，本科和高中生的努力。