Analysing wear in carpets by detecting varying local binary patterns

Currently, carpet companies assess the quality of their products based on their appearance retention capabilities. For this, carpet samples with different degrees of wear after a traffic exposure simulation process are rated with wear labels by human experts. Experts compare changes in appearance in the worn samples to samples with original appearance. This process is subjective and humans can make mistakes up to 10% in rating. In search of an objective assessment, research using texture analysis has been conducted to automate the process. Particularly, Local Binary Pattern (LBP) technique combined with a Symmetric adaptation of the KullbackLeibler divergence (SKL) are successful for extracting texture features related to the wear labels either from intensity and range images. We present in this paper a novel extension of the LBP techniques that improves the representation of the distinct wear labels. The technique consists in detecting those patters that monotonically change with the wear labels while grouping the others. Computing the SKL from these patters considerably increases the discrimination between the consecutive groups even for carpet types where other LBP variations fail. We present results for carpet types representing 72% of the existing references for the EN1471:1996 European standard.