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Article

Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation

1
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2
Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China
3
Combat Data Laboratory, Joint Logistic Support Force of PLA, Wuhan 430010, China
4
Research Institute of Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060347
Received: 24 April 2020 / Revised: 23 May 2020 / Accepted: 24 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue Virtual 3D City Models)
Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results. View Full-Text
Keywords: quasi-dense matching; descriptor; Mahalanobis distance; triangulation constraint; matching propagation quasi-dense matching; descriptor; Mahalanobis distance; triangulation constraint; matching propagation
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MDPI and ACS Style

Wang, J.; Zhang, N.; Wu, X.; Wang, W. Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation. ISPRS Int. J. Geo-Inf. 2020, 9, 347. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060347

AMA Style

Wang J, Zhang N, Wu X, Wang W. Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation. ISPRS International Journal of Geo-Information. 2020; 9(6):347. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060347

Chicago/Turabian Style

Wang, Jingxue, Ning Zhang, Xiangqian Wu, and Weixi Wang. 2020. "Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation" ISPRS International Journal of Geo-Information 9, no. 6: 347. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060347

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