Color Constancy using Achromatic Surface

被引:19
作者
Li, Bing [1 ]
Xu, De [1 ]
Xiong, Weihua [2 ]
Feng, Songhe [1 ]
机构
[1] Beijing Jiaotong Univ, Inst Comp Sci & Engn, Beijing 100044, Peoples R China
[2] OmniVis Technol, Sunnyvale, CA 95014 USA
基金
国家高技术研究发展计划(863计划);
关键词
color constancy; illumination estimation; achromatic Surface identification; ILLUMINATION CHROMATICITY; ALGORITHMS;
D O I
10.1002/col.20574
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
Although a number of elaborate color constancy algorithms have been proposed, methods such as Grey World and Max-RGB are still widely used because of their low computational costs. The Grey World algorithm is based on the grey world assumption: the average reflectance in a scene is achromatic. But this assumption cannot he always satisfied well. Borrowing on some of the strengths and simplicity of the Grey World algorithm, W. Xiong et al. proposed an advanced illumination estimation method, named Grey Surface Identification (GSI), which identifies those grey sum faces no matter what the light color is and averages them in RGB space. However, this method is camera-dependent, so it cannot be applied on the images from unknown imaging device. Motivated by the paradigm of the GSI, we present a novel iteration method to identify achromatic surface for illumination estimation. Furthermore, the local Grey Edge method is introduced to optimize the initial condition of the iteration so as to improve the accuracy of the proposed algorithm. The experiment results on different image datasets show that our algorithm is effective and outperforms sonic current state-of-the-art color constancy algorithms. (C) 2010 Wiley Periodicals, Inc. Col Res Appl, 35, 304-312, 2010; Published online 24 February 2010 in Wiley Inter Science (www.interscience.wiley. corn). DOI 10.1002/col.20574
引用
收藏
页码:304 / 312
页数:9
相关论文
共 26 条
[1]   A comparison of computational color constancy algorithms - Part I: Methodology and experiments with synthesized data [J].
Barnard, K ;
Cardei, V ;
Funt, B .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2002, 11 (09) :972-984
[2]   A comparison of computational color constancy algorithms - Part II: Experiments with image data [J].
Barnard, K ;
Martin, L ;
Coath, A ;
Funt, B .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2002, 11 (09) :985-996
[3]   A data set for color research [J].
Barnard, K ;
Martin, K ;
Funt, B ;
Coath, A .
COLOR RESEARCH AND APPLICATION, 2002, 27 (03) :147-151
[4]  
BARNARD K, 2000, P 6 EUR C COMP VIS, P275
[5]   Bayesian color constancy [J].
Brainard, DH ;
Freeman, WT .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1997, 14 (07) :1393-1411
[6]   A SPATIAL PROCESSOR MODEL FOR OBJECT COLOR-PERCEPTION [J].
BUCHSBAUM, G .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 1980, 310 (01) :1-26
[7]   Estimating the scene illumination chromaticity by using a neural network [J].
Cardei, VC ;
Funt, B ;
Barnard, K .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2002, 19 (12) :2374-2386
[8]  
Ciurea F, 2003, ELEVENTH COLOR IMAGING CONFERENCE: COLOR SCIENCE AND ENGINEERING - SYSTEMS, TECHNOLOGIES, APPLICATIONS, P160
[9]  
Ebner M, 2003, LECT NOTES COMPUT SC, V2781, P60
[10]  
Ebner M, 2004, LECT NOTES COMPUT SC, V3023, P276