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Novel Pixel-Level and Subpixel-Level Registration Algorithms for Multi-Modal Imagery Data

机译:新型的多模态图像数据像素级和亚像素级配准算法

摘要

Image registration is an important pre-processing operation to be performed before many image exploitation and processing functions such as data fusion, and super-resolution frame. Given two image frames, obtained from the same sensor or from different sensors, the registration problem involves determining the transformation that most nearly maps (or aligns) one image frame into the other. Typically, image registration requires intensive computational effort and the developed techniques are scene dependent. Furthermore, the problems of multimodal image registration (i.e. problem of registering images acquired from dissimilar sensors) and sub-pixel image registration (i.e. registering two images at sub-pixel accuracy) are highly challenging and no satisfactory solutions exist.This dissertation introduces novel techniques to solve the image registration problem both at the pixel-level and at the sub-pixel level. For pixel-level registration, a procedure is offered that enjoys the advantages that it is not scene dependent and provides the same level of accuracy for registering images acquired from different types of sensors. The new technique is based on obtaining the local frequency content of an image and using this local frequency representation to extract control points for establishing correspondence. To extract the local frequency representation of an image, a computationally efficient scheme based on minimizing the latency of a Gabor filter bank by exploiting certain biological considerations is presented. The dissertation also introduces an extension of using local frequency to solve the sub-pixel image registration problem. The new algorithm is based on using the scaled local frequency representation of the images to be registered, with computationally inexpensive scaling of the local frequency of the images prior to correlation matching. Finally, this dissertation provides a novel approach to solve the problem of multi-modal image registration. The principal idea behind this approach is to employ Computer Aided Design (CAD) models of man-made objects in the scene to permit extraction of regions-of-interest (ROI) whose local frequency representations are computed for extraction of stable matching points. Detailed performance evaluation results from an extensive set of experiments using diverse types of images are presented to highlight the strong points of the proposed registration algorithms.
机译:图像配准是在许多图像开发和处理功能(例如数据融合和超分辨率帧)之前要执行的重要预处理操作。给定从同一传感器或不同传感器获得的两个图像帧,配准问题涉及确定最接近将一个图像帧映射(或对齐)到另一个图像帧的转换。通常,图像配准需要大量的计算工作,并且所开发的技术取决于场景。此外,多模式图像配准(即从异类传感器获取图像的配准问题)和亚像素图像配准(即以亚像素精度配准两个图像)的问题具有很高的挑战性,并且不存在令人满意的解决方案。以解决像素级和子像素级的图像配准问题。对于像素级配准,提供了一种程序,该程序具有不依赖于场景的优点,并提供相同级别的精度来配准从不同类型的传感器获取的图像。新技术基于获得图像的本地频率内容,并使用此本地频率表示来提取控制点以建立对应关系。为了提取图像的局部频率表示,提出了一种基于计算的有效方案,该方案基于通过利用某些生物学考虑来最小化Gabor滤波器组的等待时间。论文还介绍了扩展使用局部频率来解决亚像素图像配准问题。新算法基于使用要配准的图像的比例缩放的局部频率表示,并且在相关匹配之前对图像的局部频率进行计算上不昂贵的缩放。最后,本文为解决多模式图像配准问题提供了一种新颖的方法。此方法背后的主要思想是采用场景中人造对象的计算机辅助设计(CAD)模型,以允许提取感兴趣区域(ROI),其区域频率表示经过计算以提取稳定的匹配点。提出了来自使用不同类型图像的大量实验的详细性能评估结果,以突出提出的注册算法的优点。

著录项

  • 作者

    Elbakary Mohamed Ibrahim;

  • 作者单位
  • 年度 2005
  • 总页数
  • 原文格式 PDF
  • 正文语种 EN
  • 中图分类

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