Integration of colour and textural information in multivariate image analysis: Defect detection and classification issues

Abstract

In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling,...) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology. Copyright © 2007 John Wiley & Sons, Ltd.