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Neural Computing and Applications

Erschienen in:

13.03.2021 | Special Issue on Multi-modal Information Learning and Analytics on Big Data

Fast overlap removal for location-related representation elements

verfasst von: Hongqian Chen, Hui Li

Erschienen in: Neural Computing and Applications | Ausgabe 5/2022

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Abstract

In order to resolve the issues with overlapping elements in location-related applications, an element adjustment method is proposed. This adjustment method can be implemented using a non-iterative algorithm which can significantly improve the processing efficiency of overlap removal. The method first sorts out all the elements according to the distance from the preset Starting Point. Repulsive offset adjustment is used for circular elements. It can be validated through theoretical derivation. Finally, the influencing factors of algorithm parameters are analyzed. Suggestions are given to further optimization of the algorithm. Experimental results show that it can effectively remove overlapping. The relative positional relationship among elements can be preserved to the greatest extent. User’s verifications and expert’s evaluation show that it can also achieve high recognition rate between the geographic region and its representation element.

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Titel: Fast overlap removal for location-related representation elements
verfasst von: Hongqian Chen
Hui Li
Publikationsdatum: 13.03.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05825-x

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