We present a collection of 24 multiple object scenes recorded under 18 multiple light source illumination scenarios each. 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, we provide also spectral characteristics of the camera, light sources, and the objects and include pixel-by-pixel ground truth annotation of uniformly coloured object surfaces. The dataset is freely available at https://github.com/visillect/mls-dataset.
Proc. SPIE. 11041, Eleventh International Conference on Machine Vision (ICMV 2018)
KEYWORDS: Image processing algorithms and systems, Light sources, Cameras, Sensors, Image segmentation, Dielectrics, Image acquisition, 3D modeling, Algorithm development, Color image segmentation, RGB color model
In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard’s DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.