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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.

Connected operators are an important tool for the analysis of greyscale images. In extending them to colour and other vector images there are a number of issues that must be addressed, including the definition of extrema, region merging criterion and the preservation of idempotence. This paper reviews the recently proposed approaches to these problems and considers some of the choices which must be made in the design of effective colour connective operators. An evaluation of the noise reduction performance resulting from these choices is presented. The use of the colour connected sieves in conjunction with the watershed transform for image segmentation is also investigated.

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