摘要
计算机视觉中的特征点匹配技术具有广泛的应用场景,包括目标跟踪、物体测距以及虚拟现实技术等。传统的特征点匹配技术只能匹配到图像中的一个目标,不适用于所有应用情况。首先介绍了传统ORB算法提取特征点和其目标匹配的效果,然后提出一种基于K-Means聚类算法的多目标特征点匹配算法,将K-Means算法应用到传统的特征点匹配算法,结合Hamming距离和RANSAC算法,使其可以匹配多个目标物。最后实验结果表明,在多目标匹配测试中,多目标匹配算法具有匹配耗时短、匹配准确性高、旋转不变性好等优点。
Feature point matching has been considered as an important and challenging research task in computer vision. It can be widely used in many applications; for example, object tracking, object measurement and virtual reality. However, most feature point matching algorithm can only be used to match single object even if there are many objects in an image. In this paper, we first introduced the traditional ORB algorithm to extract feature points and the effect of object matching. Then we presented a robust approach for multi-objects feature point matching based on K-Means. The K-Means algorithm was applied to the traditional feature point matching algorithm, which combines Hamming distance and RANSAC algorithm so that it can match multiple objects. Finally, experimental results show that the multi-objects matching algorithm has the advantages of short time, high matching accuracy, robustness to rotation and scaling in multi-objects matching test.
引文
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