Curve Segment Neighborhood-based Vector Field Exploration

Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/vis55277.2024.00075
Citations
1
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
0.25
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article

Curve Segment Neighborhood-based Vector Field Exploration

Ngoc Hoang Phan, Guoning Chen
1 citations
Image Processing and 3D Reconstruction
0.25
article

Curve Segment Neighborhood-based Vector Field Exploration

Ngoc Hoang Phan, Guoning Chen
article en
1 citations

Abstract

Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets.

University of Houston (US)
Openalex Percentile: Top 44%
Image Processing and 3D Reconstruction
0.25
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