Sergio Alejandro Orjuela Vargas

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant.

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

Robust video feature extraction invariant to natural lighting by using LBP techniques with adaptive thresholding

Real time applications in video processing require low computational cost algorithms that allow processing a considerable number, commonly 25, of frames per second. Particularly, in outdoor visual scenes, a challenge is to develop robust algorithms with environmental conditions such as natural lighting. We propose to compute an adaptive threshold based on de probability distribution of the differences in intensity between the pixels and the points on their neighborhoods when applying the LBP technique. We assume, and prove it experimentally, that such distribution is a generalized Gaussian distribution under normal conditions. We consider normal conditions a visual scene in an outdoor field composed of different objects, colors and textures. To compute the adaptive threshold, we first estimate the parameters of the generalized Gaussian distribution using the set of all differences in the image between the intensity values of pixels and points in the neighbourhood. We test the methods on four videos captures during day and night in different places in the city of Ibague. The results of this approach are of interest to determine patterns, identify objects or detect background in a further step. However, an extra step for blur correction must be still included, considering that the images of the frames at night are commonly blurred.

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.

Analyzing carpet wear

A novel 3D scanner based on structured light can extract depth information of floor coverings.