NDLI: Rigid blocks matching method based on contour curves and feature regions

Content Provider	IET Digital Library
Author	Zhao, Fuqun Zhou, Mingquan Geng, Guohua Zhu, Lipin
Abstract	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.
Starting Page	76
Ending Page	85
Page Count	10
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 1, Feb (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0392
Journal	IET Computer Vision
Publisher Date	2017-10-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Coarse Matching Computer Vision And Image Processing Technique Contour Curves Dynamic Iteration Coefficient Feature Extraction Feature Region Fine Matching Image Matching Image Recognition Image Segmentation Improved Boundary Growth Method Improved ICP Algorithm Improved Iterative Closest Point Algorithm Interpolation And Function Approximation Iterative Method Numerical Analysis Probability Probability ICP Algorithm Rigid Block Matching Method Rotation Angle Constraint Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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