The rapid development of hardware and software in the digital image processing field has boosted research in computer vision for applications in industry. The development of new electronic devices and the tendency to decrease their prices makes possible new developments that few decades ago were possible only in the imagination. This is the case of 3D imaging technology which permits to detect failures in industrial products by inspecting aspects on their 3D surface. In search of an optimal solution for scanning textiles we present in this paper a review of existing techniques for digitizing 3D surfaces. Topographic details of textiles can be obtained by digitizing surfaces using laser line triangulation, phase shifting optical triangulation, projected-light, stereo-vision systems and silhouette analysis. Although we are focused on methods that have been used in the textile industry, we also consider potential mechanisms used for other applications. We discuss the advantages and disadvantages of the evaluated methods and state a summary of potential implementations for the textile industry
The evaluation of appearance parameters is critical for quality assurance purposes when determining lifetime and/or beauty of textile products. Practical evaluations of appearance are often performed by human visual inspection, which is repetitive, exhausting, unreliable and costly. Thus, computerized automatic visual inspection has been used to alleviate those problems. Several papers have proposed objective mechanisms for quality inspection mostly using texture analysis approaches which are often not robust enough. One of the main issues for robustness of texture analysis approaches is the capability of distinguishing between similar textures. In this paper, we review, select and evaluate texture analysis approaches for distinguishing fine changes of global texture in degradation of textile floor coverings. As a result, we found that the power spectrum, local binary patterns, the texture spectrum, Gaussian Markov random fields, autoregressive models and the pseudo-Wigner distribution provide good descriptors for measuring fine changes of global texture. That is, those features can be used as starting point in applications involving fine changes of global texture, as well as a basis for the development of new methods
Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection.Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications.In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.
Video analysis in real time requires fast and efficient algorithms to extract relevant information from a considerable number, commonly 25, of frames per second. Furthermore, robust algorithms for outdoor visual scenes may retrieve correspondent features along the day where a challenge is to deal with lighting changes. Currently, Local Binary Pattern (LBP) techniques are widely used for extracting features due to their robustness to illumination changes and the low requirements for implementation. We propose to compute an automatic threshold based on the distribution of the intensity residuals resulting from the pairwise comparisons when using LBP techniques. The intensity residuals distribution can be modelled by a Generalized Gaussian Distribution (GGD). In this paper we compute the adaptive threshold using the parameters of the GGD. We present a CUDA implementation of our proposed algorithm. We use the LBPSYM technique. Our approach is tested on videos of four different urban scenes with mobilities captured during day and night. The extracted features can be used in a further step to determine patterns, identify objects or detect background. However, further research must be conducted for blurring correction since the scenes at night are commonly blurred due to artificial lighting
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