Global illumination using photon maps
Proceedings of the eurographics workshop on Rendering techniques '96
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncovering Clusters in Crowded Parallel Coordinates Visualizations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Enhancing scatterplots with smoothed densities
Bioinformatics
Visualizing Incomplete and Partially Ranked Data
IEEE Transactions on Visualization and Computer Graphics
Structuring Feature Space: A Non-Parametric Method for Volumetric Transfer Function Generation
IEEE Transactions on Visualization and Computer Graphics
Fast adaptive selection of best views
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Quantitative data visualization with interactive KDE surfaces
Proceedings of the 26th Spring Conference on Computer Graphics
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The number of modes in a kernel density estimation of a certain data distribution is strongly dependent on the chosen scale parameter. In this paper, we present an interactive mode tree visualization that allows to visually analyze the modality structure of a data distribution. Due to the branched structure of the bivariate mode tree, composed of many curved arcs in 3D, we need to utilize advanced techniques, including clutter removal through transparency, on demand outlier suppression or preservation, and best views, to improve the legibility of the visualization mapping.