一种基于角点的多模态图像配准方法

Guohua Lv, S. Teng, Guojun Lu
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引用次数: 3

摘要

提出了一种基于角点的多模态图像配准方法。该方法的动机是在多模态图像中可能出现较大的内容差异。与传统的多模态图像配准方法利用强度或梯度进行特征表示不同,我们建议使用角的曲率。此外,提出了一种新的局部描述符——边缘像素沿轮廓分布(DEPAC)来表示角的邻域。该方法将曲率和DEPAC相似度相结合,提高了配准精度。使用一组基准多模态图像和多模态显微图像,我们证明了我们提出的方法优于现有的最先进的图像配准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Multi-Modal Image Registration Method Based on Corners
This paper presents a novel method for registering multi-modal images, based on corners. The proposed method is motivated by the fact that large content differences are likely to occur in multi-modal images. Unlike traditional multi-modal image registration methods that utilize intensities or gradients for feature representation, we propose to use curvatures of corners. Moreover, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the neighborhood of corners. Curvature and DEPAC similarities are combined in our method to improve the registration accuracy. Using a set of benchmark multi-modal images and multi-modal microscopic images, we demonstrate that our proposed method outperforms an existing state-of-the-art image registration method.
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