A New Algorithm for Color-Defects Detection of Ceramic Tiles through Clustering-Based Modeling and Genetic Algorithms

In this paper, a new algorithm is proposed through the clustering-based modeling and genetic algorithms for color-defects detection of ceramic tiles. The algorithm consists of two stages: feature extraction and inspection. In the former phase, the parameters of the algorithms are regulated by using one (some) reference defect-free ceramic tile image(s) in order to produce the contextual model of colors. Then, during the inspection phase, the colors of the examined ceramic tiles images are compared to the color model; and each difference indicates a color defect. The proposed algorithm integrates a new color clustering algorithm based on min-max criterion and genetic algorithms such that the former is used by the later as the evaluation function. The experimental results on a database including 120 ceramic tiles images categorized to six groups demonstrated acceptable performance in terms of solution quality, computational burden, and adjustable sensitivity.