An evaluation of area morphology scale-spaces for colour images

Area morphological scale-spaces are widely used for hierarchical image analysis and segmentation. Despite their advantages, their extension to colour images has been restricted by the lack of an explicit order relationship for vector values. This paper presents a theoretical evaluation of two recently proposed colour sieves and their properties. It is also demonstrated that the extrema definition used by a colour sieve determines both the aggressiveness of its sieving action and its processing speed. A new colour sieve structure is introduced that attempts to capture the relative advantages of the two sieves previously studied. An objective study of the noise reduction performance of these colour sieves is presented. The segmentation performance is also analysed using the methodology provided by the Berkeley Segmentation Dataset and Benchmark, both in terms of the overall segmentation performance and its robustness to image noise. The new colour sieve is shown to have the best overall segmentation performance, and to be the most robust.