Optimal shape description using morphological signature transform via genetic algorithm

A novel method for optimal shape description based on the mnltiresolution morphological image processing is presented in this paper. The method is optimal in the sense that the optimal structuring element is determined that will enable best discrimination of object shapes. In this method a representation based on the areas of the input binary image successively eroded by multiple rotated structuring elements at different resolutions is used. For a given set of model shapes the optimal structuring element is selected by means of genetic algorithm. The optimization criteria is formulated to enable a robust shape matching. Experiments have been performed on a set of model shapes. Genetic algorithm was used to create new generations of structuring elements by crossing over the genes which represent structuring elements. The result of the iterative procedure is the optimal structuring element which was used for shape description using the morphological signature transform. The proposed optimal shape representation method is applied to the problem of shape matching which evolves in many object recognition applications. Here, an unknown object from the input image is matched to a set of known objects in order to classify it into one of finite number of possible classes. Experimental results are presented and discussed,.