Automatic Classification of Natural Stone Tiles with Computer Vision

Abstract

Classification in natural stone industry have a great importance for enterprises. There are reinstatement cases arising from the fact that ordered granite parties are not the same as the agreed sample parties at the beginning, which causes significant economic losses for the companies. There is a greater need to classify tiles using computer-aided image processing methods for the development of quality control processes that have become increasingly important due to the rapidly increasing competition and globalization in the natural stone industry. In this type of automatic systems, the attributes that give information about color and surface are extracted from the images of natural stone tiles with image processing techniques and then the data set obtained by using these attributes are classified by various artificial intelligence and data mining techniques. In this study, a classification was made on a dataset consisting of 996 pictures of natural stone tiles from six categories obtained from a natural stone producer (Beta Mermer I. C.) operating in Sivas. Gray level co-occurrence matrix (GLCM) and local binary pattern (LPB) are used to obtain pattern information of granite tiles. Several statistics related to each color channel were used to obtain color information of granites. Various datasets are created using only pattern information and combination of pattern and color information of tiles. Subsequently, classification performance of these datasets are compared using several algorithms such as, artificial neural networks, support vector machines, and naive bayes.