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A generalized multi-sensor 3D image registration and data fusion method using a multi-resolution approach.

机译：一种使用多分辨率方法的通用多传感器3D图像配准和数据融合方法。

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摘要

Recently surveillance and Automatic Target Recognition (ATR) applications have been increasing as the cost of computing power, needed to process the massive amount of information, keeps falling. Designing and implementing state-of-the-art electro-optical imaging systems to provide advanced surveillance capabilities involves integration of several technologies (i.e. precise optics, cameras, and image-computer vision algorithms for data fusion) into a programmable system. Multi-sensor fusion and integration refers to the combination of data collected from multiple sensors to provide more reliable and accurate information. Registration is the fundamental and complex process of aligning the collected data before the fusion. Several techniques for image registration have been proposed in the literature, but with limited success. In particular, one of the major limitations of existing methods is their lack of accuracy and efficiency. In addition many of these methods suffer from being applications specific. To the best of our knowledge there is no known accurate method in the literature that (a) can work under any scene circumstances/conditions and that (b) can be generalized and extended from a 2Dimensional to a 3-Dimensional space. In this research an efficient and accurate automated image registration with applications to Multi-sensor 3D LADAR imaging is presented. As we show here, the proposed approach is two-fold. First, comparison and matching of scene image/volume small patches of two overlapping 2-D or 3-D data is performed. We show here how the size of the patches is optimally derived. Second, 2D and 3-D Wavelet transforms are applied to these resulting small similar scene patches to extract a number of matching feature points. We show that the advantages of the proposed technique includes its computational efficiency, in comparison to existing methods, and its accuracy in detecting the necessary matching points, which both constitute the most fundamental/crucial but also challenging components of any data fusion/registration system. Finally, demonstration of the theories, analyses, proof of correctness behind the proposed techniques, implementation, and experimental results are presented to show the power and potential of the proposed generalized method that is extendable from 2D to 3D.
机译：近年来，监视和自动目标识别（ATR）应用程序一直在增加，因为处理大量信息所需的计算能力成本不断下降。设计和实施最先进的电光成像系统以提供高级监视功能涉及将多种技术（即精密光学，相机和用于数据融合的图像计算机视觉算法）集成到可编程系统中。多传感器融合和集成是指从多个传感器收集的数据的组合，以提供更可靠和准确的信息。配准是在融合之前对齐收集的数据的基本且复杂的过程。文献中已经提出了几种用于图像配准的技术，但是成功有限。特别地，现有方法的主要限制之一是它们缺乏准确性和效率。另外，这些方法中的许多受特定于应用的困扰。据我们所知，文献中没有一种已知的准确方法，即（a）可以在任何场景环境/条件下工作，并且（b）可以广义化并从二维空间扩展到三维空间。在这项研究中，提出了一种高效，准确的自动图像配准及其在多传感器3D LADAR成像中的应用。正如我们在此处所示，所提出的方法有两个方面。首先，进行两个重叠的2-D或3-D数据的场景图像/体积小块的比较和匹配。我们在这里展示了如何最佳地导出补丁的大小。其次，将2D和3D小波变换应用于这些结果小的相似场景补丁，以提取多个匹配的特征点。我们表明，与现有方法相比，所提出技术的优势包括其计算效率以及检测必要匹配点的准确性，这些匹配点构成了任何数据融合/注册系统的最基本/关键但又具有挑战性的组成部分。最后，对所提出的技术，实现和实验结果背后的理论，分析，正确性进行了证明，以表明所提出的可从2D扩展到3D的广义方法的功能和潜力。

著录项

  • 作者

    Bejar Colonia, Carlos.;

  • 作者单位

    University of Massachusetts Lowell.;

  • 授予单位 University of Massachusetts Lowell.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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