Image registration by using a descriptor for repetitive patterns

S. Ha, Seyun Kim, N. Cho
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引用次数: 6

Abstract

This paper proposes a new feature-based image registration method based on the description of feature clusters. This method can find larger number of correspondences than the conventional methods using singleton feature descriptors, which often fail in repetitive patterns. The reason for the failure of conventional methods in a repeating pattern is due to the existence of too many similar features, which in turn gives geometrically inconsistent matching or do not survive ratio test. Hence the proposed method follows the strategy that first separate the similar features from the repetitive patterns from the others. Then the similar features in a pattern are grouped into a set that is described by a support vector descriptor in terms of the cluster's center and radius. Once the same pattern in different images are matched, the geometric cue is added to find many geometrically consistent correspondences of the features. In the experiments, it has been demonstrated that the larger number of geometrically consistent correspondences from the repetitive pattern give more accurate registration, and thus more pleasing results in image stitching and panoramic image generation.
使用描述符对重复模式进行图像配准
提出了一种基于特征簇描述的基于特征的图像配准方法。与使用单例特征描述符的传统方法相比,该方法可以找到更多的对应关系,而单例特征描述符在重复模式中经常失败。传统方法在重复模式中失败的原因是由于存在太多的相似特征,这反过来又给出了几何上不一致的匹配或无法通过比率测试。因此,所提出的方法遵循首先将相似特征从重复模式中分离出来的策略。然后,将模式中的相似特征分组成一个集合,该集合由支持向量描述符根据聚类的中心和半径进行描述。一旦对不同图像中的相同图案进行匹配,就会添加几何线索来找到许多几何上一致的特征对应。实验表明,重复模式的几何一致性对应数量越多,配准精度越高,在图像拼接和全景图像生成中效果越好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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