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   Synthetic Data for Computational Colour Constancy Experiments

This page contains links to some of the data described in:

Kobus Barnard, Lindsay Martin, Brian Funt, and Adam Coath,
A Data Set for Colour Research,
Color Research and Application, Volume 27, Number 3, pp. 147-151, 2002.

(This is the appropriate archival reference to this data).

Questions, comments, and problems with this data should be directed to Kobus Barnard.

 Description

Camera Sensors

Information on the Sony DXC-930 calibration data can be found on the camera calibration page. This calibration data was used to derive the camera sensors used for many of our synthetic data experiments.

Illuminant Data

Important: Illuminants vary greatly in brightness. The spectral data provided here is the raw data reported by our spectrometer. For some applications, this data needs to be normalized.

The first illuminant set (image_data_sources.illum) is a set of 11 sources chosen for most of our experiments with real images. The illuminant sources were:

Sylvania 50MR16Q (12VDC)---A basic tungsten bulb
Sylvania 50MR16Q (12VDC) + Roscolux 3202 Full Blue filter
Solux 3500K (12VDC)--Emulation of daylight
Solux 3500K (12VDC)+Roscolux 3202---Emulation of daylight
Solux 4100K (12VDC)--Emulation of daylight
Solux 4100K (12VDC)+Roscolux 3202---Emulation of daylight
Solux 4700K (12VDC)--Emulation of daylight
Solux 4700K (12VDC)+Roscolux 3202---Emulation of daylight
Sylvania Warm White Fluorescent (110VAC)
Sylvania Cool White Fluorescent (110VAC)
Philips Ultralume Fluorescent (110VAC)

The second illuminant set (measured_with_sources.illum) is a larger set of measured spectra. This set consists of 81 spectra measured in and around the SFU campus at various times of the day, and in a variety of weather conditions. Unusual lighting, such as that beside neon advertising lights, was excluded. However, care was taken to include some reflected light, provided that it was not too extreme. This set of illuminants was augmented with the measurements of 21 sources, including the 11 listed above. All these illuminant sources are plausible common illuminants. Possibly more convienient for most applications is the same set normalized to maximum of one.

The third illuminant set (train.illum) was used for neural network training. To create this illuminant training set, we divided (r,g) space into cells 0.02 units wide, and placed the 11 illuminants described above into the appropriate cells. We then added illumination spectra from set of the measured spectra, provided that their chromaticity bins were not yet occupied. Finally, to obtain the desired density of coverage, we used random linear combinations of spectra from the two sets. This is justified because illumination is often the blending of light from two or more sources. In addition, to the extent that the diagonal model holds, these constructed illumination spectra will behave like physical sources with the same chromaticities as the constructed ones.

The fourth illuminant set (test.illum) was used to test trained neural networks. To produced the testing illuminant set, we used the same procedure described for the training set, but filled the space 4 times more densely. Surface Reflectance Data

The surface reflectance data (reflect_db.reflect) is a set of 1995 spectra compiled from several sources. These surfaces include the 24 Macbeth color checker patches, 1269 Munsell chips, 120 Dupont paint chips [1], 170 natural objects [1], the 350 surfaces in Krinov data set [2], and 57 additional surfaces measured by ourselves.

[1] M. J. Vrhel, R. Gershon, and L. S. Iwan, ?Measurement and Analysis of Object Reflectance Spectra,? COLOR Research and Application, vol. 19, pp. 4-9, 1994.
[2] E. L. Krinov, Spectral Reflectance Properties of Natural Formations: National Research Council of Canada, 1947.

 Data

Camera Sensors:
sensors.spect.gz (1 KB)

Illuminants Used With Image Data Experiments:
image_data_sources.illum.gz (3 KB)

Measured Illuminants:
measured_with_sources.illum.gz (26 KB)

Neural Network Training Illuminants:
train.illum.gz (23 KB)

Neural Network Testing Illuminants
test.illum.gz (74 KB)

Reflectance Database:
reflect_db.reflect.gz (472 KB)

 Publications Which Use This Data

Kobus Barnard, Brian Funt, and Vlad Cardei,
A comparison of color constancy algorithms.
Part One: Theory and experiments on synthetic data
,
IEEE Transactions on Image Processing, in press.

Kobus Barnard,
Sensor Sharpening for Computational Colour Constancy,
Submitted for publication, October 2000.

Most experiments using synthetic data in:

Kobus Barnard,
Practical Colour Constancy,
Phd thesis, Simon Fraser University, School of Computing Science, 1999.

Computational Vision Lab
Computing Science,
Simon Fraser University,
Burnaby, BC, Canada,
V5A 1S6
Fax: (778) 782-3045
Tel: (778) 782-4717
email: colour@cs.sfu.ca
Office: ASB 10865, SFU


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Last Updated: August 24, 2000