一种基于学习的自动图像配准检验方法

图像配准是众多具体应用的共性核心技术, 如图像融合, 变化检测等. 然而, 当参考图像经过变换后, 如何自动地确定变换后的图像是否与目标图像真正达到了配准仍然是目前文献中一个尚未很好解决的问题. 究其原因, 主要是很难找到一种图像相似性的度量方法来有效地对配准后的图像进行评价. 不同于传统的方法, 本文提出了一种基于学习的相似性度量方法, 即将图像配准的度量问题转化为模式分类问题, 由基于机器学习设计的分类器自动检验图像是否配准. 本文对 400 组图像进行了配准检验, 实验结果显示了该方法的可行性和可靠性. 尽管本文方法的具体实现是针对基于 Fourier-Mellin 变换的配准算法, 但这种基于学习的图像配准检验思想同样可以应用到其他配准方法中.

An Evaluation Method for Image Registration by Machine Learning

Image registration is a key step in many real applications,such as image fusion and change detection. However,after transforming the reference image,it is usually difficult to assess whether the transformed image is indeed registered with the target image.The underlying reason is that a suitable measure is unavailable currently in literature to adequately evaluate the similarity of two images.In contrast to the traditional evaluation methods,in this paper,the registration evaluation problem is converted into a classification problem through machine learning.Experiments based on 400 pairs of images demonstrate the validity and reliability of our proposed method.Although the proposed method is specifically designed for Fourier-Mellin transform based registration technique,its basic principles could also be applied to other registration techniques.