Colour morphological scale-spaces for image segmentation

Morphological scale-spaces have become an important tool for analysing greyscale images. However, their extension to colour images has proven elusive until recently. In this paper an original evaluation of two recently proposed colour sieves is presented, both algorithmically and in terms of their computational and segmentation performance. A new colour sieve structure is also proposed, motivated by the relative advantages of the two sieves previously studied. A quantitative evaluation of the segmentation performance using a set of images with human ground truth from the Berkeley dataset shows the new method to produce the best segmentation performance.