In this paper we propose a novel approach for color image segmentation. Our approach is based on segmentation of subsets of bands using mathematical morphology followed by the fusion of the resulting segmentation "channels". For color images the band subsets are chosen as RG, RB and GB pairs, whose 2D histograms are processed as projections of a 3D histogram. The segmentations in 2D color spaces are obtained using the watershed algorithm. These 2D segmentations are then combined to obtain a final result using a region split-and-merge process. The CIE L*a*b* color space is used to measure the color distance. Our approach results in improved performance and can be generalized for multiband segmentation of images such as multispectral satellite images.

MOTIVATION: Summarizing and displaying the information contained in a set of aligned sequences is an important aid to identifying patterns within the sequences. A variety of forms of consensus sequences have been used previously to provide this information. However, these methods can cause a loss of information or introduce ambiguities into the consensus sequence, and some graphical approaches may become difficult to interpret due to visual distortion. RESULTS: We have developed a method to present a more precise and graphically clear view of a consensus sequence by using an approach based on defining the major components at each position in a sequence set. The major components are given in an ordered list and their frequencies are shown as histograms which can be colour coded to reflect conservative groupings. Minor components, a one-line character-based consensus sequence and information statistics can also be presented. As well as identifying the dominant sources of variation and conservation in the sequence set, the method also enables similarities and differences between subgroups of a sequence set to be readily assessed. AVAILIABILITY: On request from the authors. CONTACT: [email protected], hxue@usthk. ust.hk