This study presents an image segmentation algorithm working on the spherical coordinates of RGB colour space. The algorithm uses a hybrid chromatic distance inspired in the human vision system which shifts from the chromatic to the greyscale distance depending on the pixel's luminance value. In dark areas of the image the chromatic distance is too sensitive to image noise, so that the greyscale distance is used instead. Colour constancy properties of this segmentation approach follow from the dichromatic reflection model. The approach is strongly robust regarding highlights and dark spots and does not need illuminant source colour normalisation. The authors give results on public benchmark image databases and robot camera images. A public implementation is made available for independent test of the algorithm image segmentation results.

A colour gradient operator defined following colour constancy preservation properties is presented. The approach does not need a priori information or changes of colour space. It is based on the angular distance between pixel colour representations in the RGB space. It is naturally invariant to intensity magnitude, implying high robustness against bright spots produced by specular reflections and dark regions of low intensity.