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.
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.
Commonly, visual inspection tasks in the textile industry are performed by human experts. The major drawback of this type of inspection is the human subjectivity, which affects accuracy and repeatability. Objectivity, accuracy and repeatability can be achieved by analysing visual characteristics of the products using computer vision. Particularly, automatic real time inspection systems based on texture analysis can be implemented using Local Binary Pattern (LBP) techniques. A recent variation of the LBP techniques, named Geometric Local Binary Pattern (GLBP) technique, showed an increase in the performance for detecting small changes of local texture. In this paper a real time implementation of the algorithm is presented by using a Graphic Processing Unit (GPU). The LBP and GLBP techniques are compared in terms of speed and accuracy while implemented on a Central Processing Unit (CPU) and GPU environments. Algorithms are tested for detecting defects in fabrics as well as for evaluating global deviations of texture, which are due to the degradation of the surface in carpets. Results show that higher discriminant power between similar textures is obtained when using the GLBP technique
Alvarez, Julio, "Cultural identity in landscape architecture, renovation of Managua's lakeside" (2005 This project provides a new lakeside area for Managua in which cultural identity in landscape architecture is represented in the use of the site and in a rescue of Managua's residents' pride in their pre-Columbian heritage. The lakeside renovation was planned using pre-Columbian design methodology and vocabulary to create a functional and environmentally sens~velandscape.
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