一种新的基于尺度适应兴趣点检测的图像匹配算法

Peng Liang, Shaofa Li, Cheng Wang
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引用次数: 0

摘要

本文研究了从图像中提取兴趣点的问题,这些兴趣点可以用于不同场景之间的可靠匹配。图像匹配主要关注两个关键点:兴趣点检测和特征匹配。然而,由于图像尺度、旋转和视角变化带来的缺点,使得特征匹配能力明显下降。本文提出了一种新的图像匹配算法,该算法在图像尺度、旋转和视角变化方面都有良好的性能。首先,我们提出了一种新的兴趣点检测器,即尺度适应哈里斯检测器,通过选择不同层次尺度上的兴趣点。在此基础上,提出了基于震源距离(EMD)的特征匹配方法。同时,我们在一个基准数据集上实现了我们提出的算法。实验结果表明,该算法在查全率和查准率方面都优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new image matching algorithm based on scale adapted interest point detection
In this paper, we study the problem of extracting interest points from images and the interest points can be used to perform reliable matching between different scenes. The image matching focuses on two key points: interest point detection and feature matching. However, due to the disadvantage brought by image scale, rotation and view change, the ability of feature matching falls off signally. This paper proposes a new image matching algorithm, which has good performances on image scale, rotation and view change. First, we bring forward a novel interest point detector called scale adapted Harris detector by selecting interest points at different levels of scales. Then we formulate the feature matching based on the Earth Mover's Distance (EMD). At the same time, we implement our proposed algorithm in a benchmark dataset. The experimental results demonstrate the proposed algorithm perform better than other methods in both recall and precision.
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