计算机科学
人工智能
匹配(统计)
图形
特征(语言学)
特征匹配
模式识别(心理学)
稳健性(进化)
特征提取
理论计算机科学
数学
语言学
统计
哲学
生物化学
化学
基因
作者
Jun Huang,Honglin Li,Yijia Gong,Fan Fan,Yong Ma,Qinglei Du,Jiayi Ma
标识
DOI:10.1109/tmm.2024.3398266
摘要
In this paper, we propose an effective method for mismatch removal, termed as graph neighborhood motion consensus, to address the feature matching problem which plays a pivotal role in various computer vision tasks. In our method, we convert each feature correspondence into a motion field sample and model it with the probabilistic graphical model (PGM). To differentiate mismatches from true matches, we firstly design a metric based on neighborhood topology consensus and neighborhood interaction to evaluate the correctness of each match. We also design a variance-based similarity search module to make the information used more reliable for better matching performance. To derive the solution of PGM, we build a model to transform the problem into an integer quadratic programming problem and obtain its closed-form solution with linear time complexity. Extensive experiments on general feature matching, fundamental matrix estimation and image registration tasks demonstrate that our proposed method can achieve superior performance over several state-of-the-art approaches.
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