Application of fuzzy mathematical morphology for unsupervised color pixels classification

In this paper, we present a new color image segmentation algorithm which is based on fuzzy mathematical morphology. After a color pixel projection into an attribute space, segmentation consists of detecting the different modes associated with homogeneous regions. In order to detect these modes, we show how a color image can be viewed as a fuzzy set with its associated membership function corresponding to a mode which is defined by a color cooccurrence matrix and by mode concavity properties. A new developed fuzzy morphological transformation is then applied to this membership function in order to identify the modes. The performance of our proposed fuzzy morphological approach is then presented using a test color image, and is then compared to the competitive learning algorithm.