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

职务名称：职业：非线性流形上的广义风格与内容分离及其在人体运动分析中的应用视觉输入是各种概念正交因子的函数。这些因素中的每一个，通常，可以表示为一个潜在的非线性流形。因此，一般来说，每个数据点都位于流形的混合上。因此，我们有一个所有这些因素的产品空间，这使得问题非常具有挑战性。然而，如果我们从概念上理解，在某种程度上，拓扑结构，维数和属性的每个单独的流形的正交因子，产生的数据，这个问题可以接近。本研究的最终目标是建立一般的数学框架，分离数据中的多个因素。特别是，在人体运动的背景下，我们的目标是建立一个数学框架，从其他来源的变化，影响视觉输入，从而利用这些模型恢复身体配置的内在身体配置。为了实现这一目标，将研究四个研究方向：1）从同一流形上的各种风格变体中学习统一的不变内容流形表示。2)学习用于给定一个或多个底层流形的表示的数据的因式分解生成模型。3)给定底层流形的表示，如何使用它来选择视觉输入中的区分特征。4)将研究结果应用于恢复内在的身体形态。风格和内容的分离问题是视觉感知的一项重要任务，也是感知的一个基本奥秘。 我们如何感知一个普通的运动，比如走路，而不管它的外观有什么变化，这一点还不清楚。本研究计划中解决的基础研究问题广泛存在于不同的计算机视觉和机器学习应用中。这些发现将有助于促进计算机视觉和机器学习领域的最新技术，并为认知科学领域的研究人员带来有趣的计算模型。人体运动分析将是本研究的主要应用领域。人体运动分析的研究具有各种重要的应用，如监控，安全，人机交互等。人体运动分析将是研究和教育活动之间的整合主题，以促进数学和科学教育。教育计划包括针对研究生、本科生和高中教育工作者和学生的若干综合活动。目标是开发教育工具，通过合作设计、实施和评估计算机视觉虚拟教室，整合PI、高中教育工作者、本科生和高中生的努力。URL：http://www.cs.rutgers.edu/~elgammal/Research/GStyleContent.htm