Automatic visual identification of correct matches in unmanned aerial vehicle images for visual-based attitude estimation

M. R. Tamjis, Samsung Lim
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Abstract

This paper presents a framework of automatic clustering to determine correctly matched keypoints locations in aerial images for visual-based attitude estimation. In this work, correct and false matches are automatically identified using a clustering technique which utilizes the outlier information to determine the initial number of clusters and cross-correlation. The proposed framework has been tested on a set of 152 Unmanned Aerial Vehicle-acquired images, and the results have been compared with the visual inspection. The comparison has shown that the proposed framework is able to provide an acceptable matching accuracy, minimize the size of region-of-interest images, and simplify the key points computation.
基于视觉姿态估计的无人机图像正确匹配的自动视觉识别
本文提出了一种自动聚类框架,用于航拍图像中正确匹配的关键点位置,用于基于视觉的姿态估计。在这项工作中,使用聚类技术自动识别正确和错误的匹配,该技术利用离群值信息来确定聚类的初始数量和相互关系。提出的框架已在一组152无人机采集的图像上进行了测试,并将结果与目视检测进行了比较。对比表明,该框架能够提供可接受的匹配精度,最小化感兴趣区域图像的大小,简化关键点的计算。
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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