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Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 6, p e0157913 (2016), PLOS ONE(11): 6
- Publication Year :
- 2015
-
Abstract
- This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.
- Subjects :
- Vertex (graph theory)
Computer science
Vector Spaces
0211 other engineering and technologies
lcsh:Medicine
Datasets as Topic
02 engineering and technology
Distance Measurement
Infographics
Planar
0202 electrical engineering, electronic engineering, information engineering
Cell Cycle and Cell Division
lcsh:Science
Measurement
Multidisciplinary
Geography
Applied Mathematics
Simulation and Modeling
Polygons
Graph
Cell Processes
Physical Sciences
Engineering and Technology
020201 artificial intelligence & image processing
Graphs
Algorithms
Research Article
Cartography
Computer and Information Sciences
Geometry
Research and Analysis Methods
Topographic Maps
Eigenvalues and eigenvectors
021101 geological & geomatics engineering
business.industry
Data Visualization
lcsh:R
Biology and Life Sciences
Pattern recognition
Cell Biology
Hierarchical clustering
Vertex (geometry)
body regions
Algebra
Linear Algebra
Polygon
Earth Sciences
lcsh:Q
Artificial intelligence
business
Eigenvectors
Mathematics
Maps as Topic
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....2c72098eabec9deb9939a44740586c3f