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Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Chasing Rainbows: A Color-Theoretic Framework for Improving and Preserving Bad Colormaps

Authors : Robert Sisneros, Mohammad Raji, Mark W. Van Moer, David Bock

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

The scientific visualization community increasingly questions the use of rainbow colormaps. This is not unfounded as significant problems are readily seen in a luminance plot of the rainbow colormap. Many good, generally applicable colormaps are proposed as direct replacements for the rainbow. However, there are still many who choose rainbows and like them. Would a colormap with perfect luminance and the chromaticity of a rainbow find a wider audience? This was our motivation in studying the range of chromatic effects arising from luminance corrections. Consequently we developed a framework for adjusting colormaps to various degrees which produces favorable results on a wide range of colormaps. In this work we will detail this framework and demonstrate its effectiveness on several colormaps.

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previous chapter Large Image Collection Visualization Using Perception-Based Similarity with Color Features
next chapter Interpolation-Based Extraction of Representative Isosurfaces
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Metadata
Title
Chasing Rainbows: A Color-Theoretic Framework for Improving and Preserving Bad Colormaps
Authors
Robert Sisneros
Mohammad Raji
Mark W. Van Moer
David Bock
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_36

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