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SUBPIXEL IMAGE REGISTRATION (IMAGE PROCESSING, IMAGE REGISTRATION, MATCHING).

机译：子像素图像注册（图像处理，图像注册，匹配）。

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摘要

This dissertation introduces and analyzes a new image registration topic--subpixel registration. Four algorithms for subpixel registration are presented and then analyzed according to their accuracy and computational requirements. One algorithm requires the resampling of an image. The accuracy of this resampling process is evaluated by comparing the frequency response of an interpolation function sampled with a high sampling frequency to an ideal low-pass filter. Using bilinear interpolation of simulated image intensities, a 0.005 pixel registration accuracy can be achieved. The four registration algorithms can be ordered according to accuracy as follows: intensity interpolation algorithm, difference method, correlation one-dimensional interpolation, correlation two-dimensional interpolation and phase correlation.; A formal mathematical description of the iterative intensity interpolation algorithm is then presented. Analyses indicate that compared to a direct intensity interpolation algorithm, the iterative algorithm saves computations by a factor of more than ten thousand. Furthermore, iterative hill-climbing algorithms (a pick-Ng coarse-search method and three climbing strategies) have been successfully used for measuring object displacements with an accuracy of 0.05 pixel. These use two successive frames of speckle images of an object and achieve an additional factor of 10 savings in computations.; This fast and accurate method for subpixel registration can be used in a variety of application areas, such as motion estimation, nondestructive evaluation, image sequence analysis, etc. It provides a way to accurately measure the displacement of individual points of a plane, without any contact or disturbance. To our knowledge, a method with these characteristics has not been previously available.; A feature-based registration algorithm using the discrete cosine transform is proposed to be used when position-dependent noise appears in images.
机译：本文介绍并分析了一个新的图像配准主题-亚像素配准。提出了四种亚像素配准算法，然后根据其准确性和计算要求进行了分析。一种算法要求对图像进行重采样。通过将以高采样频率采样的插值函数的频率响应与理想的低通滤波器进行比较，可以评估此​​重采样过程的准确性。使用模拟图像强度的双线性插值，可以实现0.005像素的配准精度。四种配准算法可以根据精度排序如下：强度插值算法，差分方法，相关一维插值，相关二维插值和相位相关。然后给出了迭代强度插值算法的形式化数学描述。分析表明，与直接强度插值算法相比，该迭代算法节省了上万倍的计算量。此外，迭代爬坡算法（pick-Ng粗糙搜索方法和三种爬坡策略）已成功用于测量对象位移，精度为0.05像素。它们使用一个对象的两个连续斑点图像帧，并在计算上节省了10倍的额外费用。这种快速，准确的亚像素配准方法可用于各种应用领域，例如运动估计，无损评估，图像序列分析等。它提供了一种无需任何操作即可精确测量平面各个点的位移的方法。接触或干扰。据我们所知，具有这些特性的方法以前尚不可用。当图像中出现位置相关的噪声时，建议使用基于特征的配准算法，该算法使用离散余弦变换。

著录项

  • 作者

    TIAN, QI.;

  • 作者单位

    University of South Carolina.;

  • 授予单位 University of South Carolina.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 1984
  • 页码 209 p.
  • 总页数 209
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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