Carpet manufacturers certify their products for end-use applications by evaluating the wear behavior of their carpets in mechanical experiments. Currently, this process is performed by visual inspection, suffering from subjective gathers that limit reliability. To automate this process, we propose the use of image processing techniques, specifically of local binary pattern (LBP) statistics. Such statistics are tolerant against illumination changes, can be easily implemented, and perform well when combined with a symmetrized adaptation of the Kullback—Leibler divergence. As a main innovation, we extend the existing rotationally invariant LBPs by including ‘mirror’ and ‘complement’ invariants. We show an accurately improved and more reliable estimation of the degree of wear in worn carpets. The evaluation is performed on four digital reference scales, each containing eight pairs of images comparing transitional degrees of wear to the original appearance. Additionally, the texture changes due to distortions of the pile yarn tufts are enhanced by choosing a suitable scale factor per reference. We validate the findings using six physical reference scales, each containing four pairs of images. In both references, linear correlations of over 0.89 are demonstrated between the degrees of wear and extracted features from the images. These findings justify the use of the proposed LBP extensions in a first approach towards an automated low-cost inspection system for carpet wear at low computation cost.

In this paper we present a novel 3D scanner to capture the texture characteristics of worn carpets into images of the depth. We first compare our proposed scanner to a Metris scanner previously attempted for this application. Then, we scan the surface of samples from the standard EN1471 using our proposed scanner. We found that our proposed scanner offers additional benefits because it has been specifically designed for carpets, performing faster, cheaper, better and also a lot more suitable for darker carpets. The results of this approach give optimistic expectations in the automation of the label assessment dealing with multiple types of carpets.

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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.

Abstract. Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations has shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique performs better to distinguish consecutive AR grades in carpets.

Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations.