Efficient Methods for Automatic Recognition With Application to Target Identification

有效的自动识别方法及其在目标识别中的应用

• 批准号: 0728929
• 负责人: Mireille Boutin
• 金额: $ 20万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728929&HistoricalAwards=false
• 关键词: Efficient; Methods; Automatic; Recognition; Application

The objective of the investigator's research is to learn how to encode the organization of complex structures for efficient classification, recognition and browsing. Complex structures such as objects, images, 3D scans and web documents are considered. The focus of the investigation is on very large datasets (e.g., several millions). Because of computational issues, such large sizes cannot be handled by existing browsing methods. As part of this research, theoretical tools and methods for addressing these issues are being developed; these tools and methods are then applied to the problem of automatically identifying a target from 3-D imaging Laser Radar (Ladar) measurements. The difficulty of indexing a large database for geometry/structure-based queries is mostly due to two factors: 1) The inherent conflict between speed and accuracy, and 2) The curse of dimensionality. The structure representations that are being developed by the investigator, which are based on invariant statistics, address these difficulties. First of all, they are fully invariant and so they allow fast data comparison by bypassing the problem of finding the best mapping between two structures. Secondly, for a generic structure, they are lossless, and so they do not compromise accuracy. Moreover, they allow for low complexity metrics, thus yielding fast comparison algorithms that are almost surely 100% accurate (as opposed to approximate algorithms, which are always approximate.) In addition, as they contain no ambiguity, it is possible to index them with high-dimensional indexing techniques that address the curse of dimensionality. Finally, they are floating-point arithmetic and low-level data friendly.

研究人员的研究目标是学习如何对复杂结构的组织进行编码，以实现有效的分类，识别和浏览。复杂的结构，如对象，图像，3D扫描和Web文档被认为是。调查的重点是非常大的数据集（例如，几百万）。由于计算问题，现有的浏览方法无法处理如此大的尺寸。 作为这项研究的一部分，正在开发解决这些问题的理论工具和方法，然后将这些工具和方法应用于从3-D成像激光雷达（激光雷达）测量中自动识别目标的问题。为基于几何/结构的查询索引大型数据库的困难主要是由于两个因素：1）速度和准确性之间的内在冲突，以及2）维数灾难。研究人员正在开发的基于不变统计的结构表示解决了这些困难。首先，它们是完全不变的，因此它们允许通过绕过找到两个结构之间的最佳映射的问题来进行快速数据比较。其次，对于通用结构，它们是无损的，因此它们不会损害准确性。此外，它们允许低复杂度度量，从而产生几乎肯定100%准确的快速比较算法（与近似算法相反，近似算法总是近似的）。此外，由于它们不包含歧义，因此可以使用解决维度灾难的高维索引技术对其进行索引。最后，它们是浮点运算和低级数据友好的。