机器视觉中角点检测算法研究

角点检测是运动检测、图像匹配、视频跟踪、三维重建和目标识别等必不可少的关键步骤,角点检测的准确性直接影响实验结果。为了更好地了解角点检测技术的发展现状,根据三种现有的角点检测方法分类对角点检测方法及相关改进进行了总结分析,并选择了FAST、SUSAN、SIFT、Shi-Tomas这几种较为典型的角点检测算法进行了实验对比,并给出了实验结果。不同的实际应用对角点检测的要求不同,不同的角点检测算法也可以相互结合,通过对现有角点检测技术的总结分析为在实际应用中对角点检测技术的选择和改进方向提供了借鉴和参考。

A Survey of Corner Detection Algorithms in Machine Vision

Corner detection is a crucial prerequisite for motion detection, image matching, video tracking, 3D reconstruction, and target recognition. The precision of corner detection has a direct bearing on the experimental outcomes. In order to better comprehend the development status of corner detection technology, the corner detection methods and associated enhancements are summarized and analyzed based on the three classifications of existing corner detection algorithms. FAST, SUSAN, SIFT, and Shi-Tomas are chosen for experimental comparison, and the results of the experiment are provided. Different practical applications have different corner detection requirements, and various corner detection algorithms can also be combined. Through a summary and analysis of the existing corner detection technology, this paper serves as a guide for the selection and development of corner detection technology for practical applications.