Lieva Van Langenhove

Automatic grading of appearance retention of carpets using intensity and range images

Textiles are mainly used for decoration and protection. In both cases, their original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, during and after manufacturing, to determine the lifetime and/or beauty of textile products. In particular, appearance retention of textile products is commonly certified with grades, which are currently assigned by human experts. However, manufacturers would prefer a more objective system. We present an objective system for grading appearance retention, particularly, for textile floor coverings. Changes in appearance are quantified by using linear regression models on texture features extracted from intensity and range images. Range images are obtained by our own laser scanner, reconstructing the carpet surface using two methods that have been previously presented. We extract texture features using a variant of the local binary pattern technique based on detecting those patterns whose frequencies are related to the appearance retention grades. We test models for eight types of carpets. Results show that the proposed approach describes the degree of wear with a precision within the range allowed to human inspectors by international standards. The methodology followed in this experiment has been designed to be general for evaluating global deviation of texture in other types of textiles, as well as other surface

Quantifying appearance retention in carpets using geometrical local binary patterns

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 have 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 is better for distinguishing consecutive AR grades in carpets.

Selection of optimal texture algorithms for evaluating degradation of carpets through experimental design

Abstract. Optimal texture analysis algorithms for describing degradation of carpets are identified. Experimental design is applied to select from a set of texture analysis algorithms those optimal for identifying texture changes due to degradation of carpets. The degree of wear of a degraded carpet is quantified by comparing its texture to the original texture. The set of texture algorithms is applied on intensity images obtained from the American and the European standards. The performance of the texture algorithms is evaluated using measures that quantify characteristics in the relationship between the metrics and the changes in texture. The statistical analysis of the experimental results shows that the local binary patterns algorithm is optimal in >50% of the cases, for describing degradation of the carpets. Other texture algorithms that optimally characterize the degradation of carpets include the use of the power spectrum, Wigner distribution, and average co-occurrence matrix algorithms.

Yarn simulations with sharp edges

This paper describes a new method for collision handling of particle based systems moving on sharp edges using intermediate points and correction methods for particles passing these edges. It aims at facilitating the simulation of physically realistic movement of the particles in contact with surfaces, even if those surfaces contain sharp edges. The method was developed in order to increase the range of surfaces that allow valid simulations. The method has for the moment only been applied to the simulation of yarn, which is represented as a one-dimensional system of particles connected by springs and dampers. This could be extended however to two-dimensional systems such as cloth, although the technique is developed for explicit integration schemes. Validation has been done by comparing with surfaces with smoothed edges as well as by comparing with actual high speed camera results. From the results, it was shown that physically realistic movement of yarn over a sharp edge can now successfully be simulated.

Measuring hairiness in carpets by using surface metrology

Recently, an automatic system for grading appearance retention in carpets using our own scanner and image analysis techniques was proposed. A system for carpets with low pile construction and without color patterns has been developed. Appearance changes in carpets with high pile construction were still not well detected. We present an approach based on surface metrology that extract information given by the hairs on the carpet surface. These features are complementary to the texture features previously explored. By combining both features, we expand the use of the automatic grading system including some carpets types with high pile construction.