Feature-guided Gaussian mixture model for image matching
作者：
Highlights：
• We propose a feature-guided Gaussian mixture model for robust feature matching.
• The method is general which can handle both rigid and non-rigid deformations.
• We provide theoretical derivations of our method and explain why and how it works.
• Our method generates more ‘true feature matches’ than other state-of-the-art methods.
• We applied the method to various matching problems in different research communities.
摘要
•We propose a feature-guided Gaussian mixture model for robust feature matching.•The method is general which can handle both rigid and non-rigid deformations.•We provide theoretical derivations of our method and explain why and how it works.•Our method generates more ‘true feature matches’ than other state-of-the-art methods.•We applied the method to various matching problems in different research communities.
论文关键词：Image matching,Feature-guided,Gaussian mixture model,Local geometric constraint,Semi-supervised EM
论文评审过程：Received 17 June 2017, Revised 6 February 2019, Accepted 1 April 2019, Available online 1 April 2019, Version of Record 5 April 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.04.001
