CAREER: Identifying Spatial and Dynamical Patterns from Images

职业：从图像中识别空间和动态模式

• 批准号: 0347456
• 负责人: Yi Ma
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347456&HistoricalAwards=false
• 关键词: CAREER; Identifying; Spatial; Dynamical; Patterns

The quantitative modeling and automatic extraction of semantic information (objects, actions, and events) from images or videos has traditionally been a difficult and intriguing problem for scientists and engineers who are interested in system automation, human machine interface, machine intelligence, and information technologies. The basis for this difficulty, and interest, is largely attributed to physical, spatial, and dynamical complexity of visual patterns (of a shape or a process): high dimensionality and inherent variability in photometry, geometry, and dynamics are just a few of such characteristics. The proposed research program aims to tackle this complexity and variability by exploiting invariant properties in spatial and dynamical patterns that can be modeled as a hierarchy of discrete and continuous symmetries in the geometry, dynamics, and physics of visual patterns commonly encountered in an urban environment. Domain knowledge from photometry, multiple-view geometry, and systems theory will be used to analytically model and study both spatial and dynamical symmetries encoded in the visual data, and efficient numerical algorithms will be developed to detect such symmetries directly from images. High-level semantics of the images or videos can therefore be identified, inferred, or learned by machines more efficiently at the level of symmetries. We envision that such a modeling paradigm will marry the benefits of both analytical modeling and statistical inference techniques and significantly reduce the complexity in modeling, analysis, and computation. It will be the key to the success of developing efficient, accurate, and robust vision systems for identifying a wide range of objects, actions, and events in an urban environment. Direct outcome of the proposed research program will be scalable algorithms that can automatically generate three-dimensional geometric models from images or efficient systems that can identify in human actions and events from a large array of video input. Such algorithms will greatly facilitate applications such as security surveillance, traffic/environmental monitoring, automatic mapping of urban areas, vision-based navigation and coordination of autonomous robots, instant sports coverage/broadcast, movie edition/video indexing, and medical image analysis. Along with an increasing interest in the relations between symmetry and perception in cognitive science and psychology, the results from this program will also help provide an analytical and computational basis for the scientific study of biological and artificial visual perception in general. On the education end, the proposed research program provides unique opportunities for the development of new interdisciplinary courses that integrate scientific methods, mathematical skills, computational techniques, and laboratory experiments across multiple scientific and engineering disciplines, including computer vision, systems theory, and machine learning. These courses will help transform and enhance future college engineering education in robotics, machine vision/learning, and image processing. The illustrative examples and computer programs to be developed can also help teach many basic and important geometric concepts to high school students or to the general public through association with common visual experience and phenomena.

对于那些对系统自动化，人机界面，机器智能和信息技术感兴趣的科学家和工程师来说，从图像或视频中对语义信息（对象，动作和事件）的定量建模和自动提取是一个困难而有趣的问题。这种困难和兴趣的基础在很大程度上归因于视觉模式的物理，空间和动态复杂性（形状或过程的）：光度法，几何形状和动力学的高维度和固有的可变性只是此类特征。拟议的研究计划旨在通过在空间和动态模式中利用不变特性来解决这种复杂性和可变性，这些属性可以建模为在Urban环境中常见的视觉模式的几何形状，动力学和物理学中的离散和连续对称性的层次结构。来自光度法，多视图几何学和系统理论的域知识将用于分析建模和研究在视觉数据中编码的空间和动态对称性，并将开发有效的数值算法来直接从图像中检测到此类对称性。因此，可以在对称水平上更有效地识别，推断或学习的图像或视频的高级语义。我们设想，这种建模范式将与分析建模和统计推理技术的好处结合，并显着降低建模，分析和计算的复杂性。这将是开发有效，准确和强大的视觉系统成功的关键，以识别城市环境中各种各样的物体，动作和事件。拟议的研究计划的直接结果将是可扩展的算法，可以从图像或有效系统中自动生成三维几何模型，这些模型可以从大量的视频输入中识别人类的行动和事件中。这种算法将极大地促进应用程序，例如安全监视，交通/环境监控，城市区域的自动映射，基于视觉的导航和自主机器人的协调，即时运动覆盖/广播，电影版/视频索引和医疗图像分析。除了对认知科学和心理学中对称性与感知之间的关系越来越兴趣外，该计划的结果还将有助于为一般的生物学和人工视觉感知的科学研究提供分析和计算基础。在教育端，拟议的研究计划为开发新的跨学科课程提供了独特的机会，这些课程将科学方法，数学技能，计算技术和实验室实验整合到了多个科学和工程学科，包括计算机视觉，系统理论和机器学习。这些课程将有助于改造和增强未来的大学工程学教育，机器人愿景/学习和图像处理。要开发的说明性示例和计算机程序还可以通过与共同的视觉体验和现象相关联，向高中生或公众传授许多基本和重要的几何概念。