Llumination estimation combining physical and statistical approaches

Authors:
Yuan Jia-zheng;Tian li-yan;Bao Hong;Huang Jing-hua;Zhang Rui-zhe
Affiliations:
Institute of Information Technology, Beijing Union University, Beijing, China;College of Application Science & Technology, Beijing, Union University, Beijing, China;Institute of Information Technology, Beijing Union University, Beijing, China;Institute of Information Technology, Beijing Union University, Beijing, China;Institute of Information Technology, Beijing Union University, Beijing, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

Citing 5
Cited 0

A novel algorithm for color constancy

International Journal of Computer Vision
Color by Correlation: A Simple, Unifying Framework for Color Constancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data

IEEE Transactions on Image Processing
A comparison of computational color constancy Algorithms. II. Experiments with image data

IEEE Transactions on Image Processing
Edge-Based Color Constancy

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Illumination estimation for color constancy is an important problem in computer vision. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, the advantages of the two kinds are integrated. At first, a novel statistic-based algorithm called Illumination Estimation using K-nearest-neighbor (IE-KNN) is proposed. And then the physics-based Grey-Edge algorithm is used to extract image features for IEKNN. One of the most important aims of this paper is to reduce the feature dimension in traditional statistics-based approaches. The experimental results show that this combined physical and statistical algorithm is effective and can achieved much better color constancy result.