Automatic image segmentation using fuzzy hit or miss and homogeneity index

This paper proposes an automatic image segmentation algorithm. Our hierarchical algorithm uses recursive segmentation that consists of two major steps. First, local thresholding is carried out by the fuzzy hit-or-miss operator, which allows dynamic separation of a grey-scale image into two classes, based on local intensity distributions. The fuzzy hit-or-miss, being an operator of fuzzy mathematical morphology, plays an important role in performing the dynamic local segmentation. This operator gives a better shape description than global thresholding methods. It also retains small but significant regions in satellite images. Second, the homogeneity index is measured in each class based on the quality of normalized intra-region uniformity. The proposed method has been tested using both synthetic and satellite images successfully; moreover, the algorithm can estimate the number of classes automatically.