Bernard, Jürgen ; Dobermann, Eduard ; Bögl, Markus ; Röhlig, Martin ; Vögele, Anna ; Kohlhammer, Jörn (2016)
Visual-Interactive Segmentation of Multivariate Time Series.
EuroVA 2016, 7th international EuroVis workshop on Visual Analytics. Groningen, The Netherlands (June 6-7, 2016)
doi: 10.2312/eurova.20161121
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Bernard, Jürgen ; Dobermann, Eduard ; Bögl, Markus ; Röhlig, Martin ; Vögele, Anna ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual-Interactive Segmentation of Multivariate Time Series |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2016 |
| Verlag: | Eurographics Association, Goslar |
| Veranstaltungstitel: | EuroVA 2016, 7th international EuroVis workshop on Visual Analytics |
| Veranstaltungsort: | Groningen, The Netherlands |
| Veranstaltungsdatum: | June 6-7, 2016 |
| DOI: | 10.2312/eurova.20161121 |
| Kurzbeschreibung (Abstract): | Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data. |
| Freie Schlagworte: | Guiding Theme: Digitized Work, Guiding Theme: Individual Health, Guiding Theme: Smart City, Research Area: Computer graphics (CG), Research Area: Computer vision (CV), Research Area: Human computer interaction (HCI), Information visualization, Visual analytics, Time series analysis, Data mining, Machine learning, Clustering, Human motion analysis |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 08 Mai 2019 06:40 |
| Letzte Änderung: | 08 Mai 2019 06:40 |
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