Convolutional Neural Network approaches to granite tiles classification

The quality control process in stone industry is a challenging problem to deal with nowadays. Due to the similar visual appearance of different rocks with the same mineralogical content, economical losses can happen in industry if clients cannot recognize properly the rocks delivered as the ones initially purchased. In this paper, we go toward the automation of rock-quality assessment in different image resolutions by proposing the first data-driven technique applied to granite tiles classification. Our approach understands intrinsic patterns in small image patches through the use of Convolutional Neural Networks tailored for this problem. Experiments comparing the proposed approach to texture descriptors in a well-known dataset show the effectiveness of the proposed method and its suitability for applications in some uncontrolled conditions, such as classifying granite tiles under different image resolutions.