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Outlier Robust Feature Correspondence by Learning Based Matching Process
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作者 YANG Xu ZENG Shaofeng LIU Zhiyong 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2024年第4期1351-1367,共17页
Feature correspondence is a crucial aspect of various computer vision and robot vision tasks.Unlike traditional optimization-based matching techniques,researchers have recently adopted a learning-based approach for ma... Feature correspondence is a crucial aspect of various computer vision and robot vision tasks.Unlike traditional optimization-based matching techniques,researchers have recently adopted a learning-based approach for matching,but these methods face challenges in dealing with outlier features.This paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier issue.Additionally,the authors introduce a modified cross-entropy matching loss to handle the outlier problem.As a result,the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier features.Benchmark experiments confirm the effectiveness of the proposed approach. 展开更多
关键词 Feature correspondence graph neural network optimal transport problem pointmatching
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