Multiple Light Source Dataset for Colour Research
This dataset addresses the need for realistic evaluation scenarios in computational color science and computer vision, though it is incremental as it builds on existing data collection efforts.
The authors created a dataset of 24 object scenes under 18 different light source conditions to evaluate computational color constancy algorithms, providing spectral data and pixel-level ground truth annotations for benchmarking color-based image segmentation.
We present a collection of 24 multiple object scenes each recorded under 18 multiple light source illumination scenarios. The illuminants are varying in dominant spectral colours, intensity and distance from the scene. We mainly address the realistic scenarios for evaluation of computational colour constancy algorithms, but also have aimed to make the data as general as possible for computational colour science and computer vision. Along with the images of the scenes, we provide spectral characteristics of the camera, light sources and the objects and include pixel-by-pixel ground truth annotation of uniformly coloured object surfaces thus making this useful for benchmarking colour-based image segmentation algorithms. The dataset is freely available at https://github.com/visillect/mls-dataset.