Photo-Guided Exploration of Volume Data Features

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dc.contributor.authorRaji, Mohammaden_US
dc.contributor.authorHota, Aloken_US
dc.contributor.authorSisneros, Roberten_US
dc.contributor.authorMessmer, Peteren_US
dc.contributor.authorHuang, Jianen_US
dc.contributor.editorAlexandru Telea and Janine Bennetten_US
dc.date.accessioned2017-06-12T05:12:22Z
dc.date.available2017-06-12T05:12:22Z
dc.date.issued2017
dc.description.abstractIn this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target. The algorithm resulting from our research allows one to ask the question of whether features like those in the target image exists in a given dataset. In that way, our method is one of imagery query or reverse engineering, as opposed to manual parameter tweaking of the full visualization pipeline. For target images, we can use real-world photographs of physical phenomena. Our method leverages deep neural networks and evolutionary optimization. Using a trained similarity function that measures the difference between renderings of a phenomenon and real-world photographs, our method optimizes rendering parameters. We demonstrate the efficacy of our method using a superstorm simulation dataset and images found online. We also discuss a parallel implementation of our method, which was run on NCSA's Blue Waters.en_US
dc.description.sectionheadersExploratory Techniques
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20171091
dc.identifier.isbn978-3-03868-034-5
dc.identifier.issn1727-348X
dc.identifier.pages31-39
dc.identifier.urihttp://dx.doi.org/10.2312/pgv.20171091
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20171091
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectScientific visualization
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.titlePhoto-Guided Exploration of Volume Data Featuresen_US
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EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization