Open AccessRigid blocks matching method based on contour curves and feature regions
Author(s) -
Zhao Fuqun,
Zhou Mingquan,
Geng Guohua,
Zhu Lipin
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0392
Subject(s) - centroid , matching (statistics) , feature (linguistics) , boundary (topology) , artificial intelligence , rigid transformation , computer science , iterative closest point , pattern recognition (psychology) , algorithm , rotation (mathematics) , point (geometry) , feature extraction , mathematics , computer vision , point cloud , geometry , mathematical analysis , linguistics , statistics , philosophy
This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.