Name
Abstract | ||
|---|---|---|
Numerous attempts have been made to define a color space and a color distance metric that would closely resemble the human color vision. The uniformity has been the main challenge, the human vision system is more sensitive to some colors while less sensitive to others. A distance given by an ideal metric would match the color difference seen by the human vision system. This study attempts to define such a metric utilizing the spectral data and the available information on the distinguishable colors. Deep neural networks are used in metric learning for modeling the color space and the metric. The resulting metric is then tested against the standard CIEDE2000 metric. DNNs are also used to project spectral data onto a new color space. The results indicate that the new color space with the Euclidean metric is more perceptually uniform than the standard LAB color space with the CIEDE2000 metric. The new metric enables better understanding about the human vision system and measuring the color differences. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EUVIP.2018.8611776 | 2018 7th European Workshop on Visual Information Processing (EUVIP) |
Keywords | Field | DocType |
standard LAB color space,standard CIEDE2000 metric,resulting metric,distinguishable colors,metric utilizing,human vision system,human color vision,color distance metric,color difference,deep metric learning | Computer vision,Color space,Machine vision,Computer science,Human visual system model,Euclidean distance,Artificial intelligence,Deep learning,Color vision,Color difference,Lab color space | Conference |
ISSN | ISBN | Citations |
2164-974X | 978-1-5386-6898-6 | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fedor Zolotarev | 1 | 0 | 0.34 |
| Arto Kaarna | 2 | 174 | 27.50 |