A Combined Fuzzy Clustering -Neuron Approach in the Segmentation of Non-uniform Color Surfaces

Computational intelligence theories offer, individually, different potentials to solve real world problems. However, fusion of these potentials provides opportunities to generate more real world robust systems. Cosmetic inspection of possible non-uniform surfaces found in manufacturing is a challenge to human inspectors. This paper deals with the proposal of a new hybrid methodology to segment color images in order to detect non-uniform regions that may appear in manufactured goods. The hybrid methodology combines two fuzzy clustering algorithms, the FCM and the GG, and a SOM ANN. Because of its properties the FCM is used to find the optimal number of clusters of a sample population of nonuniform surfaces. This value is then used to initialize the GG algorithm to determine the best centroids that represents the color population. Finally a SOM is trained with the results of the GG to perform the segmentation. Findings show that the proposed methodology generates color regions in accordance to a quality inspection criterion. The proposed methodology is also compared against the performance of the FCM to show the advantages of the hybrid methodology.