by Joachim Staib, Sebastian Grottel, Stefan Gumhold
Abstract:
Scatterplots directly depict two dimensions of multi-dimensional data points, discarding all other information. To visualize all data, these plots are extended to scatterplot matrices, which distribute the information of each data point over many plots. Problems arising from the resulting visual complexity are nowadays alleviated by concepts like filtering and focus and context. We present a method based on depth of field that contains both aspects and injects information from all dimensions into each scatterplot. Our approach is a natural generalization of the commonly known focus effects from optics. It is based on a multidimensional focus selection body. Points outside of this body are defocused depending on their distance. Our method allows for a continuous transition from data points in focus, over regions of blurry points providing contextual information, to visually filtered data. Our algorithm supports different focus selection bodies, blur kernels, and point shapes. We present an optimized GPU-based implementation for interactive exploration and show the usefulness of our approach on several data sets.
Reference:
Enhancing Scatterplots with Multi-Dimensional Focal Blur (Joachim Staib, Sebastian Grottel, Stefan Gumhold), In Computer Graphics Forum, volume 35, 2016.
Bibtex Entry:
@article{CGF:CGF12877,
author = {Joachim Staib and Sebastian Grottel and Stefan Gumhold},
title = {Enhancing Scatterplots with Multi-Dimensional Focal Blur},
journal = {Computer Graphics Forum},
volume = {35},
number = {3},
issn = {1467-8659},
url = {http://dx.doi.org/10.1111/cgf.12877},
doi = {10.1111/cgf.12877},
pages = {11--20},
year = {2016},
projects = {SCADS},
areas = {areava},
abstract = {Scatterplots directly depict two dimensions of multi-dimensional data points, discarding all other information. To visualize all data, these plots are extended to scatterplot matrices, which distribute the information of each data point over many plots. Problems arising from the resulting visual complexity are nowadays alleviated by concepts like filtering and focus and context. We present a method based on depth of field that contains both aspects and injects information from all dimensions into each scatterplot. Our approach is a natural generalization of the commonly known focus effects from optics. It is based on a multidimensional focus selection body. Points outside of this body are defocused depending on their distance. Our method allows for a continuous transition from data points in focus, over regions of blurry points providing contextual information, to visually filtered data. Our algorithm supports different focus selection bodies, blur kernels, and point shapes. We present an optimized GPU-based implementation for interactive exploration and show the usefulness of our approach on several data sets.}
}