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Analyzing parameter influence on time-series segmentation and labeling.

, , , , , and . IEEE VAST, page 269-270. IEEE Computer Society, (2014)

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Supporting an Early Detection of Diabetic Neuropathy by Visual Analytics., , , , , , , , and . EuroVA@EuroVis, Eurographics Association, (2014)Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography., , , , , and . Information, 10 (9): 266 (2019)Visual Analysis of Optical Coherence Tomography Data in Ophthalmology., , , , and . EuroVA@EuroVis, page 37-41. Eurographics Association, (2017)Detection of Diabetic Neuropathy - Can Visual Analytics Methods Really Help in Practice?, , and . EuroRV³@EuroVis, page 19-21. Eurographics Association, (2016)Grid-based Exploration of OCT Thickness Data of Intraretinal Layers., , , , , and . VISIGRAPP (3: IVAPP), page 129-140. SciTePress, (2019)Sequencing of categorical time series., , , and . VAST, page 213-214. IEEE Computer Society, (2015)Visibility Widgets: Managing Occlusion of Quantitative Data in 3D Terrain Visualization., and . VINCI, page 51-58. ACM, (2016)Visual-Interactive Segmentation of Multivariate Time Series., , , , , and . EuroVA@EuroVis, page 31-35. Eurographics Association, (2016)Feature-Driven Visual Analytics of Chaotic Parameter-Dependent Movement., , , , , and . Comput. Graph. Forum, 34 (3): 421-430 (2015)Lightweight Coordination of Multiple Independent Visual Analytics Tools., , , , , , and . VISIGRAPP (3: IVAPP), page 106-117. SciTePress, (2019)