Natural image database and its use for scene illuminant estimation

This paper describes a database collecting natural color images, called the Natural Image Database, and shows how it is used for illuminant estimation problems. First, we present significant features of the database. This database is not only a collection of color images of natural scenes, but also includes scene illuminant spectra and camera calibration data. Because these data sets are available via the Internet at http://www.osakac.ac.jp/labs/shoji, the database can be used as the calibrated common test data for evalu- ating the performance of illuminant estimation algorithms. Second, we focus on two illuminant estimation methods, the subspace method for estimating scene illuminant spectra and the sensor cor- relation method for classifying illuminant color temperature. The al- gorithms are briefly described. The former is based on the linear model representation of possible illuminant spectra, and the latter is based on correlation computation between an observed image and the reference illuminant gamuts. The performance of these algo- rithms is evaluated using the database. © 2002 SPIE and IS&T.

[1]  B. Wandell,et al.  Natural scene-illuminant estimation using the sensor correlation , 2002, Proc. IEEE.

[2]  D H Brainard,et al.  Bayesian color constancy. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[3]  S. J.P..  Characteristic spectra of Munsell colors , 2002 .

[4]  Ron Gershon,et al.  Measurement and Analysis of Object Reflectance Spectra , 1994 .

[5]  M. H. Brill,et al.  Contributions to the theory of invariance of color under the condition of varying illumination , 1981 .

[6]  Shoji Tominaga,et al.  MULTICHANNEL VISION SYSTEM FOR ESTIMATING SURFACE AND ILLUMINATION FUNCTIONS , 1996 .

[7]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[8]  Graham D. Finlayson,et al.  Color by Correlation , 1997, CIC.

[9]  B A Wandell,et al.  Scene illuminant classification: brighter is better. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Deane B. Judd.  Sensibility to Color-Temperature Change as a Function of Temperature* , 1933 .

[11]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[12]  J. Cohen,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulas , 1968 .

[13]  J. Cohen.  Dependency of the spectral reflectance curves of the Munsell color chips , 1964 .

[14]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[15]  Mark S. Drew,et al.  Color constancy from mutual reflection , 1991, International Journal of Computer Vision.

[16]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[17]  M. D'Zmura,et al.  Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces. , 1993, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Brian V. Funt,et al.  Is Machine Colour Constancy Good Enough? , 1998, ECCV.

[19]  Brian A. Wandell,et al.  The Synthesis and Analysis of Color Images , 1992, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  L. Maloney.  Evaluation of linear models of surface spectral reflectance with small numbers of parameters. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[21]  J. Parkkinen,et al.  Characteristic spectra of Munsell colors , 1989 .

[22]  Brian V. Funt,et al.  Colour by Correlation in a Three-Dimensional Colour Space , 2000, ECCV.

[23]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .