离群值
人工智能
模式识别(心理学)
特征(语言学)
Kullback-Leibler散度
计算机科学
相对运动
匹配(统计)
熵(时间箭头)
图像配准
点集注册
计算机视觉
特征提取
聚类分析
由运动产生的结构
数学
运动估计
点(几何)
图像(数学)
统计
哲学
语言学
物理
几何学
量子力学
机械
作者
Feng Shao,Zhaoxia Liu,An J
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-12
被引量:9
标识
DOI:10.1109/tgrs.2021.3068185
摘要
Accurate point matching is widely used, and it is a critical and challenging process in feature-based image registration. To improve feature matching accuracy on putative matches with heavy outliers and similar local structures, an accurate and robust feature point matching algorithm based on minimum relative motion entropy (MRME) is proposed, in which the relative motion between the putative matches and their K-nearest neighbors is formulated. Based on the relative motion clustering result, the relative motion entropy is defined to find the coincident relative motions. According to relative motions with MRME, the outliers are removed in a two-stage feature match strategy. With quasi-linear time complexity, outliers with random or irregular relative motion are removed efficiently and accurately, while inliers with coincident relative motion are retained. Three data sets with repetitive patterns, viewpoint changes, low overlapping areas, and local deformations are used to demonstrate the performance of the proposed algorithm. MRME is shown to be more robust and accurate than ten state-of-the-art feature matching algorithms.
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