Sergio Orjuela Vargas, Ewout Vansteenkiste, Filip Rooms, Wilfried Philips, Benhur Ortiz-Jaramillo, Simon De Meulemeester, Lieva Van Langenhove, Robain De Keyser
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
When performing image analysis, one of the most critical steps is the selection of appropriate techniques. A
huge amount of features can be extracted from several techniques and the selection is commonly performed
based on expert knowledge. In this paper we present the theory of experimental designs as a tool for an
objective selection of techniques in image analysis domain. We present a study case for evaluating appearance
retention in textile floor coverings using texture features. The use of experimental design theory permitted to
select an optimal set of techniques for describing the texture changes due to degradation.
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 Kullback-
Leibler 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.
Carpet manufacturers certify their products with labels corresponding to the capability of the carpets in retaining
the original appearance. Traditionally, these labels are subjectively defined by reference cases where
human experts evaluate the degree of wear, which is quantified by a number called the wear label. Industry is
very interested in converting these traditional standards to automated objective standards. With this purpose,
research has been conducted using image analysis with either depth or intensity data. In this paper, we present
a comparison of texture features extracted from both types of images. For this, we scanned 3D data and photographed
eight types of images provided from the EN1471 standard. The features are extracted comparing the
distribution of Local Binary Patterns (LBPs) computed from images of original and change in appearance. We
assess how well we can arrange the features in the order of the wear labels and count the number of consecutive
wear labels that can be statistically distinguished. We found that two of the eight carpet types are properly
described using depth data and five using intensity data while one type could not be described. These results
suggest that both types of images can be complementary used for representing the wear labels. This can lead to
an automated and universal labeling system for carpets.
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.
Image degradation is a frequently encountered problem in different
imaging systems, like microscopy, astronomy, digital photography, etc. The degradation is usually modeled as a convolution with a blurring kernel (or Point Spread Function, psf) followed by noise addition. Based on the combined knowledge about the image degradation and the statistical features of the original images, one is able to
compensate at least partially for the degradation using so-called image restoration algorithms and thus retrieve information hidden for the observer. One problem is that often this blurring kernel is unknown, and has to be estimated before actual image
restoration can be performed. In this work, we assume that the psf can be modeled by a function with a single parameter, and we estimate the value of this parameter. As an example of such a single-parametric psf, we have used a Gaussian. However, the method is generic and can be applied to account for more realistic degradations, like optical defocus, etc.
We present a novel method for joint estimation of the degradation and restoration of photon-limited images. Our method will be demonstrated on confocal microscope images, since confocal microscopy is an important tool in many academic (fundamental biology, . . . ) and industrial (material science, pharmaceutical industry, . . . )
applications. However, the observed images are usually degraded, which hinders analysis and interpretation of the images. Degradation in this kind of images is due to two sources: first, we have blurring due to the bandlimited nature of the optical system; second, Poisson noise contaminates the observations due to the discrete nature of the photon detection process.
The proposed method iterates noise reduction and blur estimation using the steerable pyramid transform (which is a variant of the wavelet transform) and deconvolution in the signal domain. These steps are applied in two phases, a training phase and a restoration phase. In the first phase, these three steps are iterated until
the blur estimation converges. The second phase is the actual restoration phase.
During the iterations the blur estimation serves as a sharpness measure for the restored image, and is used to
controls the number of iterations. So, our integrated method provides a completely automatic algorithm where no prior information about the image degradation is required. Our integrated technique was compared with other common restoration techniques for these kind of images, and provided the best restoration results, with least artifacts.
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