Open AccessA morphing approach for continuous generalization of linear map features
Author(s) -
Aji Gao,
Jingzhong Li,
Kai Chen
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0243328
Subject(s) - morphing , interpolation (computer graphics) , generalization , context (archaeology) , linear interpolation , cartographic generalization , computer science , similarity (geometry) , matching (statistics) , scale (ratio) , topological skeleton , active shape model , artificial intelligence , shape analysis (program analysis) , mathematics , algorithm , computer vision , geometry , pattern recognition (psychology) , motion (physics) , mathematical analysis , image (mathematics) , geography , cartography , segmentation , static analysis , statistics , archaeology , programming language
With the development of web maps, people are no longer satisfied with fixed and limited scale map services but want to obtain personalized and arbitrary scale map data. Continuous map generalization technology can be used to generate arbitrary scale map data. This paper proposes a morphing method for continuously generalizing linear map features using shape context matching and hierarchical interpolation (SCM-HI). More specifically, shape characteristics are quantitatively described by shape context on which shape similarity is measured based on a chi-square method; then, two levels of interpolation, skeleton and detail interpolations, are employed to generate the geometry of intermediate curves. The main contributions of our approach include (1) exploiting both the geometry and spatial structure of a vector curve in shape matching by using shape context, and (2) preserving both the main shape structure as-rigid-as-possible and local geometric details as gradual and smooth as possible for intermediate curves by hierarchical interpolation. Experiments show that our method generates plausible morphing effects and can thus serve as a robust approach for continuous generalization of linear map features.