2018
Zeyen, Max; Post, Tobias; Hagen, Hans; Ahrens, James; Rogers, David; Bujack, Roxana
Color Interpolation for Non-Euclidean Color Spaces Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
Abstract | Links | BibTeX | Tags: visualization, visualization techniques
@inproceedings{zeyen2018interpolation,
title = {Color Interpolation for Non-Euclidean Color Spaces},
author = {Max Zeyen and Tobias Post and Hans Hagen and James Ahrens and David Rogers and Roxana Bujack},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/ColorInterpolationforNon-EuclideanColorSpaces.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.},
keywords = {visualization, visualization techniques},
pubstate = {published},
tppubtype = {inproceedings}
}
Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.
Bujack, Roxana; Turton, Terece; Rogers, David; Ahrens, James
Ordering Perceptions about Perceptual Order Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
Abstract | Links | BibTeX | Tags: visualization, visualization techniques
@inproceedings{bujack2018ordering,
title = {Ordering Perceptions about Perceptual Order},
author = {Roxana Bujack and Terece Turton and David Rogers and James Ahrens},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/OrderingPerceptionsaboutPerceptualOrder.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.},
keywords = {visualization, visualization techniques},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.
: . .
1.
Zeyen, Max; Post, Tobias; Hagen, Hans; Ahrens, James; Rogers, David; Bujack, Roxana
Color Interpolation for Non-Euclidean Color Spaces Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
@inproceedings{zeyen2018interpolation,
title = {Color Interpolation for Non-Euclidean Color Spaces},
author = {Max Zeyen and Tobias Post and Hans Hagen and James Ahrens and David Rogers and Roxana Bujack},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/ColorInterpolationforNon-EuclideanColorSpaces.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.
2.
Bujack, Roxana; Turton, Terece; Rogers, David; Ahrens, James
Ordering Perceptions about Perceptual Order Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
@inproceedings{bujack2018ordering,
title = {Ordering Perceptions about Perceptual Order},
author = {Roxana Bujack and Terece Turton and David Rogers and James Ahrens},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/OrderingPerceptionsaboutPerceptualOrder.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.