Improved SURF−FLANN feature extraction and matching algorithm for video stitching of fully-mechanized working face

The SURF (Speed Up Robust Features) feature extraction algorithm and FLANN (Fast Library or Approximate Nearest Neighbors) feature matching algorithm in current video stitching technology have the problems of feature point extraction errors and low feature point matching accuracy in harsh environmen...

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Bibliographic Details
Published inMéitàn xuébào Vol. 50; no. 6; pp. 3224 - 3234
Main Authors Qinghua MAO, Menghan WANG, Xin HU, Jiao ZHAI
Format Journal Article
LanguageChinese
Published Editorial Office of Journal of China Coal Society 01.06.2025
Subjects
feature extraction
feature matching
flann algorithm
fully-mechanized working face
surf algorithm
video stitching
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ISSN0253-9993
DOI10.13225/j.cnki.jccs.2023.1560

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Summary:The SURF (Speed Up Robust Features) feature extraction algorithm and FLANN (Fast Library or Approximate Nearest Neighbors) feature matching algorithm in current video stitching technology have the problems of feature point extraction errors and low feature point matching accuracy in harsh environments of fully-mechanized working face. An improved SURF−FLANN feature extraction and matching algorithm for video stitching of fully-mechanized working face is proposed. To improve the accuracy of feature point extraction, the improved algorithm extracts SURF key feature points of video images by changing conventional Gaussian filter to advanced bilateral filter, and improves the descriptor operator by adding feature point 4−domain feature point descriptor information to the feature vector. This improvement further improves the description of feature points. To improve the speed of feature point matching, The R−FLANN (Random sample consensus-Fast library or approximate nearest neighbors) feature matching algorithm is
ISSN:0253-9993
DOI:10.13225/j.cnki.jccs.2023.1560