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|a 10.1109/TVCG.2024.3382607
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|a eng
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1 |
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|a Huang, Xuan
|e verfasserin
|4 aut
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| 245 |
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|a Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data
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|c 2024
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|a Date Revised 01.05.2024
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|a Citation Status Publisher
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|a Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback
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|a Journal Article
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1 |
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|a Miao, Haichao
|e verfasserin
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| 700 |
1 |
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|a Kim, Hyojin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Townsend, Andrew
|e verfasserin
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| 700 |
1 |
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|a Champley, Kyle
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tringe, Joseph
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|4 aut
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| 700 |
1 |
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|a Pascucci, Valerio
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1 |
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|a Bremer, Peer-Timo
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|u http://dx.doi.org/10.1109/TVCG.2024.3382607
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