Open AccessImproved RANSAC features image‐matching method based on SURF
Author(s) -
Liu Jinliang,
Bu FanLiang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9198
Subject(s) - ransac , image stitching , scale invariant feature transform , artificial intelligence , matching (statistics) , robustness (evolution) , computer science , computer vision , feature matching , pattern recognition (psychology) , feature (linguistics) , image matching , feature extraction , image (mathematics) , mathematics , statistics , biochemistry , chemistry , linguistics , philosophy , gene
For the speed of traditional SIFT algorithm in the feature extraction and matching is slow, the article proposes an improved RANSAC features image matching method based on speeded up robust features (SURF). First of all, detect images features and extract with SURF method, use the fast library for approximate nearest neighbours‐based matcher method to perform initial matching on image feature points. Improve the RANSAC algorithm to increase the probability of correct matching points being sampled. Experimental results show that the improved RANSAC algorithm has high matching accuracy, good robustness, and short running time. It lays the foundation for the subsequent fast image stitching.