概率逻辑
稳健性(进化)
计算机科学
概括性
可扩展性
人工智能
采样(信号处理)
网络拓扑
相似性(几何)
匹配(统计)
拓扑(电路)
数据挖掘
机器学习
模式识别(心理学)
数学
计算机视觉
统计
心理学
生物化学
化学
滤波器(信号处理)
组合数学
数据库
图像(数学)
心理治疗师
基因
操作系统
作者
Zaixing He,Chentao Shen,Xinyue Zhao
标识
DOI:10.1016/j.patcog.2024.110293
摘要
Feature point matching between two images is a fundamental and important process in machine vision. In many cases, mismatches are inevitable, and removing mismatches is an indispensable task. The existing methods attempt to find comprehensive constraints or sampling model to achieve better performance, which results in the increasingly complexity and may cause the weakness of the generality and scalability. To address this issue, a method called Local Topology similarity guided probabilistic Sampling consensus (LTS) is proposed. It constructs a topological network, then quantifies the mismatch probability in a concise approach based on comparing the topological relationship with neighbourhoods. Then, it detects and removes the mismatches by sampling guided by the mismatch probability. Compared with the state-of-the-art methods, LTS has an excellent performance in accuracy and robustness.
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