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2015 IEEE Pacific Visualization Symposium (PacificVis) (2015)
Hangzhou, China
April 14, 2015 to April 17, 2015
ISBN: 978-1-4673-6879-7
pp: 223-230
Ayan Biswas , The Ohio State University, USA
David Thompson , Mississippi State University, USA
Wenbin He , The Ohio State University, USA
Qi Deng , University of Florida, USA
Chun-Ming Chen , The Ohio State University, USA
Han-Wei Shenk , The Ohio State University, USA
Raghu Machiraju , The Ohio State University, USA
Anand Rangarajan , University of Florida, USA
Although vortex analysis and detection have been extensively investigated in the past, none of the existing techniques are able to provide fully robust and reliable identification results. Local vortex detection methods are popular as they are efficient and easy to implement, and produce binary outputs based on a user-specified, hard threshold. However, vortices are global features, which present challenges for local detectors. On the other hand, global detectors are computationally intensive and require considerable user input. In this work, we propose a consensus-based uncertainty model and introduce spatial proximity to enhance vortex detection results obtained using point-based methods. We use four existing local vortex detectors and convert their outputs into fuzzy possibility values using a sigmoid-based soft-thresholding approach. We apply a majority voting scheme that enables us to identify candidate vortex regions with a higher degree of confidence. Then, we introduce spatial proximity- based analysis to discern the final vortical regions. Thus, by using spatial proximity coupled with fuzzy inputs, we propose a novel uncertainty analysis approach for vortex detection. We use expert's input to better estimate the system parameters and results from two real-world data sets demonstrate the efficacy of our method.
Detectors, Uncertainty, Entropy, Tensile stress, Robustness, Electronic mail, Data visualization

A. Biswas et al., "An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity," 2015 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hangzhou, China, 2015, pp. 223-230.
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