In this paper we use a support vector machine (SVM) for defects identification in textile. This n... more In this paper we use a support vector machine (SVM) for defects identification in textile. This new approach serves in the fast detection and extraction of fabric defects from the images of textile fabric based on geometrical analysis of the textile pattern images. Actually, most defects arising in the production process of textile material are still detected by human inspection [1] and the work of inspectors is very tedious and time consuming. They have to detect small details that can be located; the identification rate is about 70%. In addition, the effectiveness of visual inspection decreases quickly with fatigue. For these reasons, an algorithm has been proposed for the defect identification and classification. So, the images analyzed came from an artificial vision system that we used to acquire and memorize those images under jpeg format. The vision system is composed of a camera with a sensor 512X512 Pixels. The classifier SVM was manipulated to classify all defects in the defected fabric.
International Journal of Advanced Engineering Research and Science, 2016
We present an access control application that enables the authentication of the members of a buil... more We present an access control application that enables the authentication of the members of a building, this application uses a biometric watermarking algorithm based on a new robust method that uses various techniques such as, Eigen face, QR code, contourlet transform. Several attacks have been applied to the watermarking algorithm, and they have proven its robustness. The tests performed on the graphical interface, showed the smooth running of the implementation of access control.
Journal of Signal Processing Theory and Applications, 2012
Defect in textiles are generated in woven fabric due to improper treatments in weaving machines, ... more Defect in textiles are generated in woven fabric due to improper treatments in weaving machines, spinning errors and inadequate preparations of fiber at the spinning stage. The aim of this work is the detecting of the small surface defects which appear as local anomalies embedded in a homogeneous texture. The methodology used in this paper is based on the morphological operators contained in toolbox of Mathematical Morphology developed in the software MATLAB. The extraction of the window defect in original image is obtained. A geometric features where calculated in order to identify the textile defect type (size and form). The result obtained show that the combination of the morphological operation and the geometric features approaches can give better results for the identification of the type of defects in homogeneous texture.
American Journal of Physics and Applications, 2014
In a complex and changing a remote sensing system, which requires taking quick and informed decis... more In a complex and changing a remote sensing system, which requires taking quick and informed decisions environment, connectionist methods have shown their great contribution in particular the reduction and classification of spectral data. In this context, this paper proposes to study the parameters that optimize the results of an artificial neural network ANN multilayer perceptron based, for classification of chemical agents on multi-spectral images. The mean squared error cost function remains one of the major parameters of the network convergence at its learning phase and a challenge that will face our approach to improve the gradient descent by the conjugate gradient method that seems fast and efficient.
In this paper we use a support vector machine (SVM) for defects identification in textile. This n... more In this paper we use a support vector machine (SVM) for defects identification in textile. This new approach serves in the fast detection and extraction of fabric defects from the images of textile fabric based on geometrical analysis of the textile pattern images. Actually, most defects arising in the production process of textile material are still detected by human inspection [1] and the work of inspectors is very tedious and time consuming. They have to detect small details that can be located; the identification rate is about 70%. In addition, the effectiveness of visual inspection decreases quickly with fatigue. For these reasons, an algorithm has been proposed for the defect identification and classification. So, the images analyzed came from an artificial vision system that we used to acquire and memorize those images under jpeg format. The vision system is composed of a camera with a sensor 512X512 Pixels. The classifier SVM was manipulated to classify all defects in the defected fabric.
International Journal of Advanced Engineering Research and Science, 2016
We present an access control application that enables the authentication of the members of a buil... more We present an access control application that enables the authentication of the members of a building, this application uses a biometric watermarking algorithm based on a new robust method that uses various techniques such as, Eigen face, QR code, contourlet transform. Several attacks have been applied to the watermarking algorithm, and they have proven its robustness. The tests performed on the graphical interface, showed the smooth running of the implementation of access control.
Journal of Signal Processing Theory and Applications, 2012
Defect in textiles are generated in woven fabric due to improper treatments in weaving machines, ... more Defect in textiles are generated in woven fabric due to improper treatments in weaving machines, spinning errors and inadequate preparations of fiber at the spinning stage. The aim of this work is the detecting of the small surface defects which appear as local anomalies embedded in a homogeneous texture. The methodology used in this paper is based on the morphological operators contained in toolbox of Mathematical Morphology developed in the software MATLAB. The extraction of the window defect in original image is obtained. A geometric features where calculated in order to identify the textile defect type (size and form). The result obtained show that the combination of the morphological operation and the geometric features approaches can give better results for the identification of the type of defects in homogeneous texture.
American Journal of Physics and Applications, 2014
In a complex and changing a remote sensing system, which requires taking quick and informed decis... more In a complex and changing a remote sensing system, which requires taking quick and informed decisions environment, connectionist methods have shown their great contribution in particular the reduction and classification of spectral data. In this context, this paper proposes to study the parameters that optimize the results of an artificial neural network ANN multilayer perceptron based, for classification of chemical agents on multi-spectral images. The mean squared error cost function remains one of the major parameters of the network convergence at its learning phase and a challenge that will face our approach to improve the gradient descent by the conjugate gradient method that seems fast and efficient.
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Papers by Roukhe ahmed