Color correction: a novel weighted Von Kries model based on memory colors

Authors:
Alejandro Moreno;Basura Fernando;Bismillah Kani;Sajib Saha;Sezer Karaoglu
Affiliations:
Color in Informatics and Media Technology;Color in Informatics and Media Technology;Color in Informatics and Media Technology;Color in Informatics and Media Technology;Color in Informatics and Media Technology
Venue:
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Year:
2011

Citing 4
Cited 1

Random Forests

Machine Learning
Applying Visual Object Categorization and Memory Colors for Automatic Color Constancy

ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A 3D-polar coordinate colour representation well adapted to image analysis

SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Computer Graphics in Spain: a Selection of Papers from CEIG 2009: Compositing images through light source detection

Computers and Graphics

Color constancy using single colors

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present an automatic color correction framework based on memory colors. Memory colors for 3 different objects: grass, snow and sky are obtained using psychophysical experiments under different illumination levels and later modeled statistically. While supervised image segmentation method detects memory color objects, a luminance level predictor classifies images as dark, dim or bright. This information along with the best memory color model that fits to the data is used to do the color correction using a novel weighted Von Kries formula. Finally, a visual experiment is conducted to evaluate color corrected images. Experimental results suggest that the proposed weighted von Kries model is an appropriate color correction model for natural images.