• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Xu Huaxun, Ma Qianli, Cai Xun, and Li Sikun. The Topology Voronoi Graph of Visualizing Local Vector Field[J]. Journal of Computer Research and Development, 2011, 48(4): 666-674.
Citation: Xu Huaxun, Ma Qianli, Cai Xun, and Li Sikun. The Topology Voronoi Graph of Visualizing Local Vector Field[J]. Journal of Computer Research and Development, 2011, 48(4): 666-674.

The Topology Voronoi Graph of Visualizing Local Vector Field

More Information
  • Published Date: April 14, 2011
  • Topology visualization methods are very important for discovering the topology structure of the fluid fields. Among them, topology graph is a primary means, which can describe the topology relation among the critical points clearly. However, it is incapable of depicting the scope of the topology feature in the fluid field. As a basic property of topology feature, the scope property is important to investigate the fluid field and its time-dependent transformation. This paper presents a new method named topology Voronoi graph which is able to define the scope and describe the trend of one topology feature in the time-dependent vector field. First, we introduce the streamline distance to describe the mutual influence between any two points in the vector field. Then we can calculate the streamline distance of each point, with respect to the critical point it crossed, to define the feature region for the critical points in the vector field. Finally, we design an efficient topology Voronoi graph generation algorithm. The experiments on the wind field and the synthetic function data show that this method can enhance the structure visualization effects on the fluid field.
  • Related Articles

    [1]Guo Husheng, Liu Yanjie, Wang Wenjian. Concept Drift Processing Method of Streaming Data Based on Mixed Feature Extraction[J]. Journal of Computer Research and Development, 2024, 61(6): 1497-1510. DOI: 10.7544/issn1000-1239.202330184
    [2]Zhang Xian, Shi Canghong, Li Xiaojie. Visual Feature Attribution Based on Adversarial Feature Pairs[J]. Journal of Computer Research and Development, 2020, 57(3): 604-615. DOI: 10.7544/issn1000-1239.2020.20190256
    [3]Chen Jiaying, Yu Jiong, Yang Xingyao. A Feature Extraction Based Recommender Algorithm Fusing Semantic Analysis[J]. Journal of Computer Research and Development, 2020, 57(3): 562-575. DOI: 10.7544/issn1000-1239.2020.20190189
    [4]Fang Rongqiang, Wang Jing, Yao Zhicheng, Liu Chang, Zhang Weigong. Modeling Computational Feature of Multi-Layer Neural Network[J]. Journal of Computer Research and Development, 2019, 56(6): 1170-1181. DOI: 10.7544/issn1000-1239.2019.20190111
    [5]Zhang Huijie, Liu Yaxin, Ma Zhiqiang, He Xinting, Bao Ning. A Terrain Skeleton Feature Extraction Method Based on Morphological Encoding[J]. Journal of Computer Research and Development, 2015, 52(6): 1409-1423. DOI: 10.7544/issn1000-1239.2015.20131422
    [6]Ma Qianli, Li Sikun, Zeng Liang. Visualization of Multi-Shock Features for Unstructured-Grid Flows Based on Two-Level Sampling[J]. Journal of Computer Research and Development, 2012, 49(7): 1450-1459.
    [7]Tian Mei, Luo Siwei, Huang Yaping, and Zhao Jiali. Extracting Bottom-Up Attention Information Based on Local Complexity and Early Visual Features[J]. Journal of Computer Research and Development, 2008, 45(10): 1739-1746.
    [8]Chen Gang and Chen Xinmeng. An Audio Feature Extraction Method Taking Class Information into Account[J]. Journal of Computer Research and Development, 2006, 43(11): 1959-1964.
    [9]Zheng Yujie, Yang Jingyu, Xu Yong, and Yu Dongjun. A New Feature Extraction Method Based on Fisher Discriminant Minimal Criterion[J]. Journal of Computer Research and Development, 2006, 43(7): 1201-1206.
    [10]Zhang Hongyun, Miao Duoqian, and Zhang Dongxing. Analysis and Extraction of Structural Features of Off-Line Handwritten Digits Based on Principal Curves[J]. Journal of Computer Research and Development, 2005, 42(8): 1344-1349.

Catalog

    Article views (800) PDF downloads (401) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return