SGER ACT: Stochastic Shape Analysis for Recognizing and Tracking Objects in Images and Videos

SGER ACT：用于识别和跟踪图像和视频中的对象的随机形状分析

• 批准号: 0345242
• 负责人: Washington Mio
• 金额: $ 10万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0345242&HistoricalAwards=false
• 关键词: SGER; ACT; Stochastic; Shape; Analysis

Imaging devices have become ubiquitous tools of surveillance of public areas, remote locations, areas of restricted access, and other sites where additional security is needed. The detailed analysis of collected images can provide invaluable information about people, objects, their characteristics and patterns of behavior. Thus, combined with other strategies, image analysis can contribute significantly to the prevention of terrorism and national security. However, the execution of this task poses challenging problems due to the vast amount of imagery generated by surveillance devices. To make this task feasible, advanced automated systems are needed to screen images and route to human operators only material that is very likely to contain relevant information. The proposed interdisciplinary research addresses problems on the interface of shape and digital image analysis, whose solutions will contribute to the implementation of such intelligent surveillance systems, and will be useful in numerous other applications. Images contain information about two main attributes of objects: their shapes and textures. The proposers will develop a novel framework to represent and analyze planar shapes quantitatively using methods and tools of differential geometry, differential topology, and statistics. Statistical texture analysis and synthesis will be combined with the study of shapes to produce finer models of imaged objects. New algorithms of shape and image analysis will be developed, implemented, and applied to: (a) the detection and recognition of objects in noisy images; (b) tracking dynamic shapes possibly subject to occlusions in video sequences; (c) the organization of large databases of shapes for efficient retrieval and processing of information. Current techniques of algorithmic shape analysis are somewhat limited in scope or performance: some represent shapes using coarse collections of landmarks whose selection may be difficult to automate, and some involve heavy computational costs. Computational efficiency issues also limit the use of existing methods of image analysis; in spite of the remarkable success that methods based on partial differential equations have had in many applications, computational costs associated with typical implementations are high and the performance is not adequate for applications in video surveillance. There is a pressing need for efficient, robust algorithms that can analyze, process, and simulate the dynamics of shapes of continuous closed curves. The main idea proposed here is the use of computational stochastic differential geometry to study shapes, i.e., the algorithmic analysis of differential geometric representations of continuous curves in a statistical framework. The proposers will: (i) analyze closed shapes by representing them as elements of infinite-dimensional Riemannian manifolds via their angle or curvature functions; (ii) develop geometry-based tools for statistical inference problems on shape spaces; (iii) derive techniques for nonlinear filtering and tracking of shapes in infinite-dimensional shape manifolds; (iv) study completions of contours and textures with the goal of discovering hidden geometric features that follow an observable pattern; (v) implement algorithms and apply them to the solution of problems in shape and image analysis. The key new element in this approach is the use of the geometry of spaces of curves to study shapes, not only the geometry of individual curves. Results originating from this research may have far-reaching implications in shape, image and video analysis. The proposed algorithmic approach to shapes has the potential to set a new paradigm for the treatment of curve evolution. The team has expertise in the areas of differential geometry and topology, statistics, computing, and image analysis. This grouping reflects the interdisciplinary nature of the proposed investigation and will further enhance the atmosphere of collaborative research that exists among the PIs and their graduate students. Moreover, the applications to be investigated will contribute to the education and involvement of more students in areas related to national security. The PIs will continue to develop and offer courses and seminars from the introductory to the advanced levels targeting a broad audience of science students with the goal of increasing the overall impact of this line of research. To encourage the participation of undergraduates and students from underrepresented groups, motivated students will have full access to the Florida State University Laboratory of Computational Vision, where a hands-on learning environment will allow them to explore the area with their own experiments. To disseminate research results the proposers will continue to publish articles in well-circulated journals, post results in various electronic preprint archives, produce multimedia presentations on CD-ROMs, write introductory articles in magazines or handbooks, and present results at regional, national and international conferences.This award is supported jointly by the NSF and the Intelligence Community. The Approaches to Combat Terrorism Program in the Directorate for Mathematical and Physical Sciences supports new concepts in basic research and workforce development with the potential to contribute to national security.

成像设备已成为公共区域，偏远地区，限制访问区域以及其他需要额外安全性的其他站点的无处不在的工具。对收集图像的详细分析可以提供有关人，对象，其特征和行为模式的宝贵信息。因此，与其他策略相结合，图像分析可以为防止恐怖主义和国家安全做出重大贡献。但是，由于监视设备产生的大量图像，该任务的执行构成了具有挑战性的问题。为了使这项任务可行，需要先进的自动化系统来筛选图像和路由到人类操作员，只有很可能包含相关信息的材料。拟议的跨学科研究解决了形状和数字图像分析界面的问题，其解决方案将有助于实施这种智能监视系统，并将在许多其他应用中有用。图像包含有关对象的两个主要属性的信息：它们的形状和纹理。提议者将开发一个新颖的框架，以使用差异几何，差异拓扑和统计的方法和工具来代表和分析平面形状。统计纹理分析和合成将与形状的研究结合使用，以产生更精细的成像对象模型。形状和图像分析的新算法将被开发，实现和应用于：（a）噪声图像中对象的检测和识别； （b）跟踪动态形状可能会受到视频序列中的遮挡； （c）大型形状数据库的组织，以有效地检索和处理信息。当前的算法形状分析技术在范围或性能方面有些限制：有些代表了使用粗糙的地标收集的形状，其选择可能难以自动化，有些则涉及繁重的计算成本。计算效率问题还限制了现有图像分析方法的使用；尽管在许多应用中具有基于部分微分方程的方法具有显着的成功，但与典型实现相关的计算成本仍然很高，并且性能不足以适合视频监视中的应用。迫切需要有效，可靠的算法，可以分析，处理和模拟连续封闭曲线的形状动力学。这里提出的主要思想是使用计算随机微分几何形状来研究形状，即，在统计框架中连续曲线的差异几何表示算法分析。提议者将：（i）通过将封闭形状表示为通过其角度或曲率函数表示无限二维流形的元素来分析封闭形状； （ii）开发基于几何的工具，用于形状空间上的统计推断问题； （iii）得出无线性滤波和跟踪无限二维形状歧管形状的技术； （iv）研究轮廓和纹理的完成，目的是发现遵循可观察模式的隐藏几何特征； （v）实现算法并将其应用于形状和图像分析中问题的解决方案。这种方法中的关键元素是使用曲线空间的几何形状来研究形状，而不仅仅是单个曲线的几何形状。源自这项研究的结果可能对形状，图像和视频分析具有深远的影响。所提出的形状算法方法有可能为曲线演化的处理设定新的范式。该团队在差异几何和拓扑，统计，计算和图像分析领域具有专业知识。该分组反映了拟议的调查的跨学科性质，并将进一步增强PIS及其研究生之间存在的合作研究的气氛。此外，要进行调查的申请将有助于更多学生参与与国家安全有关的领域。 PI将继续开发并提供从入门介绍的课程和研讨会到针对广泛的科学专业学生的高级级别，目的是增加这一研究的整体影响。为了鼓励本科生和来自代表性不足的团体的学生的参与，积极进取的学生将完全访问佛罗里达州立大学计算愿景实验室，在该实验室中，动手学习环境将使他们可以通过自己的实验来探索该领域。为了传播研究结果，提议者将继续在流通期刊上发表文章，发布各种电子预印本档案的结果，在CD-ROM上产生多媒体演示，在杂志或手册中撰写介绍性文章，并在区域，国民和国际会议上介绍结果。这些奖项由NSF和Intelligence Socizemente and Intelligence Commusence Sopprence。在数学和物理科学局中打击恐怖主义计划的方法支持基础研究和劳动力发展中的新概念，有可能为国家安全做出贡献。