Abstract

Local feature extraction and description algorithms can be used to address a large variety of computer vision problems, including pattern recognition, frame stitching and 3D reconstruction. One of the most computationally expensive and time-consuming stages of any method based on local features is keypoint matching. This paper discusses possible ways to optimize matching for frame alignment and object detection applications using Vantage-Point tree indexing to optimize pairwise keypoint matching, and image keypoint graph indexing to optimize pose estimation.

中文翻译：

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实时应用中用于图像匹配和目标检测的局部特征描述符索引

摘要

局部特征提取和描述算法可用于解决各种各样的计算机视觉问题，包括模式识别，帧拼接和3D重建。基于局部特征的任何方法中，计算上最昂贵，最耗时的阶段之一就是关键点匹配。本文讨论了使用Vantage-Point树索引优化成对关键点匹配以及图像关键点图形索引来优化姿势估计的针对帧对齐和对象检测应用优化匹配的可能方法。