特征(语言学)
匹配(统计)
人工智能
模式识别(心理学)
欧几里德距离
稳健性(进化)
Blossom算法
计算机视觉
计算机科学
点(几何)
点集注册
特征匹配
算法
特征提取
数学
统计
哲学
语言学
生物化学
化学
几何学
基因
作者
Qiangxian Huang,Tao Xiang,Zhihao Zhao,Kui Wu,Hongli Li,Rongjun Cheng,Liansheng Zhang,Zhenying Cheng
摘要
Feature point matching is one of the fundamental tasks in binocular vision. It directly affects the accuracy and quality of 3D reconstruction. This study proposes a directional region-based feature point matching algorithm based on the SURF algorithm to improve the accuracy of feature point matching. First, same-name points are selected as the matching reference points in the left and right images. Then, the SURF algorithm is used to extract feature points and construct the SURF feature point descriptors. During the matching process, the location relationship between the query feature point and the reference point in the left image is directed to determine the corresponding matching region in the right image. Then, the matching is completed within this region based on Euclidean distance. Finally, the grid-based motion statistics algorithm is used to eliminate mismatches. Experimental results show that the proposed algorithm can substantially improve the matching accuracy and the number of valid matched points, particularly in the presence of a large amount of noise and interference. It also exhibits good robustness and stability.
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