Linear colour segmentation revisited
This work addresses colour segmentation in computer vision, but it is incremental as it modifies existing algorithms with specific improvements.
The authors tackled the problem of linear colour segmentation by proposing a new algorithm modification based on a region adjacency graph framework and introducing a colour space projective transform for better handling of shadows and highlights. They tested it on a benchmark dataset of 31 natural scene images with pixel-by-pixel ground truth, showing qualitative advantages over other model-based algorithms.
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.