Probabilistic relaxation method for line-drawing interpretation

2003 ◽  
Author(s):  
O. Hori ◽  
S. Shimotsuji ◽  
F. Hoshino ◽  
T. Ishii
Keyword(s):  
Line Drawing ◽  
Relaxation Method ◽  
Probabilistic Relaxation ◽  
Line Drawing Interpretation

Line-drawing interpretation using probabilistic relaxation

1993 ◽  
Vol 6 (2-3) ◽  
pp. 100-109 ◽  
Author(s):  
Osamu Hori ◽  
Shigeyoshi Shimotsuji ◽  
Fumihiko Hoshino ◽  
Toshiaki Ishii
Keyword(s):  
Line Drawing ◽  
Probabilistic Relaxation ◽  
Line Drawing Interpretation

scholarly journals A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation

2020 ◽  
Vol 9 (9) ◽  
pp. 509
Author(s):  
Zejun Zuo ◽  
Lin Yang ◽  
Xiaoya An ◽  
Wenjie Zhen ◽  
Haoyue Qian ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Road Network ◽  
Road Networks ◽  
Relaxation Method ◽  
Primary Objective ◽  
Coordinate Systems ◽  
Matching Method ◽  
Urban Road ◽  
Probabilistic Relaxation ◽  
Relaxation Matching

The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional “node-arc” unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks.

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