Advances on multivariate image analysis for product quality monitoring

An increasing number of industrial applications requires visual inspection of products. Computer vision provides consolidated tools for reliable and fully automatic characterization and classification of the product quality at relatively low costs. One of such powerful tool is multivariate image analysis (MIA). In the MIA procedure as proposed in [1] is considered, that is well suited for texture analysis. To extend the performance of the MIA procedure in [1] to the analysis of wider spatial domains and to improve the algorithm from the computational point of view, a new formulation, named iMIA, has been recently proposed in [2]. The main contribution of the present paper is a modification of the iMIA algorithm that, by exploiting fast Fourier transform filtering, allows a considerable reduction of the computational time when spatial neighborhoods larger than few pixels are considered. Secondly, a different texture characterization with respect to [2] is proposed, to further extend the algorithm range of applicability. The characterization is based on histograms of textural features [3]. The algorithm is tested on two case studies in the field of texture analysis, namely, classification of rice quality, where the different characterization of texture allows a great improvement with respect to [2], and the characterization of nanofiber assemblies.