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A morphing approach for continuous generalization of linear map features.
Gao, Aji; Li, Jingzhong; Chen, Kai.
Afiliación
  • Gao A; School of Resource and Environment Sciences, Wuhan University, Wuhan, China.
  • Li J; School of Resource and Environment Sciences, Wuhan University, Wuhan, China.
  • Chen K; Chengdu Research Institute of Surveying and Investigation, Chengdu Sichuan, China.
PLoS One ; 15(12): e0243328, 2020.
Article en En | MEDLINE | ID: mdl-33290396
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: China
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