Multi-scale adaptive corner detection and feature matching algorithm for UUV task target image

Jian Xu, Xingyu Zhou, Xiaoyuan Chen, Mingze Xu
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Abstract

In order to ensure the collected images can be matched exactly when UUV operates autonomously underwater. An improved Harris-FAST algorithm is proposed in this paper. The algorithm takes full advantages of FAST algorithm and Harris algorithm, which makes the threshold value with self-adaptive and the detected corner in multi-scale and omnibearingly. It can overcome the corner information loss and position offset caused by corner detection in single-scale and optimize the non-maxima suppression. After completing the corner detection, using feature descriptor to generate eigenvector and complete the corner matching and elimination of mismatching based on Euclidean distance method. UUV simulation test system of binocular vision is built in this paper, and underwater images are captured. Through the contrast test, it can verify the validity that the proposed algorithm is used in underwater images.
UUV任务目标图像的多尺度自适应角点检测与特征匹配算法
为了保证无人潜航器在水下自主运行时所采集到的图像能够准确匹配。本文提出了一种改进的Harris-FAST算法。该算法充分利用了FAST算法和Harris算法的优点,使阈值具有自适应能力,并能在多尺度、全方位地检测到角点。克服了单尺度角点检测带来的角点信息丢失和位置偏移,优化了非极大值抑制。完成角点检测后,利用特征描述子生成特征向量,完成基于欧氏距离法的角点匹配和错配消除。本文建立了UUV双目视觉仿真测试系统,并采集了水下图像。通过对比测试,验证了该算法在水下图像中应用的有效性。
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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