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Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou (2019)
Article de journal

In industry, the welding inspection is considered as a mandatory stage in the process of quality assurance/quality control. This inspection should satisfy the requirements of the standards and codes governing the manufacturing process in order to prevent unfair harm to the industrial plant in construction. For this purpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographic testing is presented, where a succession of operations of preprocessing, image segmentation, feature extraction and finally defects classification is carried out on radiographic images. The last operation which is the main contribution in this paper consists in an unsupervised classifier based on a finite mixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newly applied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGD-based mixture model are estimated using the Expectation-Maximization algorithm where, exact computations of mean and shape parameters are originally provided. The weld defect database represent four weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by a shape geometric descriptor composed of geometric measures. An outstanding performance of the proposed mixture model, compared to the one using the multivariate Gaussian distribution, is shown, where the classification rate is improved by 3.2% for the whole database, to reach more than 96%. The efficiency of the proposed classifier is mainly due to the flexible fitting of the input data, thanks to the MGGD shape parameter.Voir les détails

Mots clés : Mixture model, Multivariate GGD, radiography, weld defect, classification

Fast Adapting Mixture Parameters Schemes for Probability Density Difference-Based Deformable Model

Aicha Baya Goumeidane, Nafaa Nacereddine (2019)
Article de journal

This paper presents a new region-driven active contour using the pdf difference to evolve. The pdf estimation is done via a new and fast Gaussian mixture model (GMM) parameters updating scheme. The experiments performed on synthetic and X-ray images have shown not only an accurate contour delineation but also outstanding performance in terms of execution speed compared to the GMM estimation based on EM algorithm and to non-parametric pdf estimations.Voir les détails

Mots clés : Active contour, Adaptive mixture, GMM parameters update

Missing data restoration of sinogram in limited-angle computed tomography

a.benammar, a.allag, N.Mazouz, R.Drai, M.Yahi (2019)
Article de conférence

We present in this work the limited-angle computed tomography, which is an ill-posed inversion problem. This case often exists in the industry to allow faster non-destructive testing during production phase. However, the inspection is difficult to achieve due to the shape and size of the inspected parts. During the last decade, various approaches were proposed for case of limited-angle. These methods were developed for medical application use and do not take into account physical limitations specific to industrial materials. The aim of this work is to propose a method, which permits to recover the missing data in the acquisition of projections using minimizing a function. We tested our method with sinogram obtained from Shepp-Logan phantom containing missing projections. The reconstruction image of inpainted sinogram achived using FBP method and Iterative cimmino method. The results clearly show that the proposed method can retrieve accurate information that leads to a better-reconstructed image.Voir les détails

Mots clés : image reconstruction, FBP, Cimmino, Inpainting, Missing projections

Contribution à l’amélioration des performances du codage turbo dans les systèmes de transmission numériques

Brahim OUDJANI (2018)
Thèse de doctorat

Pour bénéficier des propriétés des codes LDPC (Low-Density-Parity-Check) et Turbo Convolutional Codes (TCC), nous proposons un codage concaténé de type Gallager/Convolutionnel codé de la manière turbo. Le code modifié crée un équilibre entre les avantages et les inconvénients de LDPC et TCC en termes de complexité globale et de latence. Cela se fera à travers deux décodeurs SISO différents; LDPC et code convolutif récursif systématique (RSC) du même taux de code R= 1/2 sans entrelaceur. Étant donné que les deux décodeurs SISO sont de natures différentes, ils échangent des informations extrinsèques qui seront facilement adaptées l’une à l’autre. L'étude de la complexité de calcul et des performances de décodage sur un canal AWGN indique qu'une telle approche conduit à d'excellentes performances en raison de plusieurs facteurs. L'approche proposée réalise un compromis entre les régions de convergence et de plancher d'erreur. Il réduit la complexité de décodage par rapport au TCC et au 3D-TCC. Il fournit un meilleur gain de codage sur LDPC et PCGC (Parallel Concatenated Gallager Codes). Ces caractéristiques assureront un rapport coût-performance optimal. Comme ils peuvent être un meilleur choix pour les systèmes de communication d'aujourd'hui. Voir les détails

Mots clés : Complexité de calcul; Code convolutif; Information extrinsèque; LDPC; Concaténation parallèle; Turbo code.

A Bayesian Mumford–Shah Model for Radiography ImageSegmentation

N. Ramou, N. Chetih, M. Halimi (2018)
Article de journal

This paper investigates the segmentation of radiographic images using a level set method based on a BayesianMumford–Shahmodel. The objective is to separate regions in an image that have very close arithmetic means, where a model based on thestatistical mean is not effective. Experimental results show that the proposed model can successfully separate such regions,in both synthetic images and real radiography images.Voir les détails

Mots clés : Level set

Performance of some Variational ImplicitDeformable Models on Segmenting OpticalMicroscopy Images

Y. Boutiche, N. Cheteh, N. Ramou (2018)
Article de conférence

Industrial micrographs are used to evaluate a steelsor alloys. This assessment consists of visualizing and describingthe basic element (at the nanoscale) constituting the material.The information provided by the micrographic images needto be highlighted by image processing methods. In this paperthe performance of some region-based variational models arepresented. Such study allows to choose best models that give themore accuracy segmentation in less processing time.Voir les détails

Mots clés : Material microstructures, Microscopy images, segmentation, deformable models, region-based active contours

Video Processing Software-based Pipeline Endoscopic Inspection

Nadia MHAMDA, Nafaa Nacereddine, Aissa Boulmerka (2018)
Article de conférence

Currently, all the codes and the standards of the fluids transport industries require rigorous pipeline inspection, in order to detect all defects and anomalies and avoid leaks and failures. For this reason, a team within the division of Signal Processing and Imagery had as mission to develop an endoscope which can replace the operator inspection inside the pipeline and improve its quality and diagnostic. This endoscope named 'Pipe Explorer' is controlled by FPGA microcontrollers, and is equipped with a camera. While moving inside the pipe, the camera records a video on the memory card. In this way and in order to offer a practical tool to the operator, we have developed graphical software based on processing techniques of the stored video consisting in video preprocessing and segmentation. At the end of this processing, we obtain a video result on which appears the analysis and the interpretation of the original video to give an internal pipe quality diagnosis. The results shows all the defective areas such as corrosion which are stained with {green, blue, red} color according to its degree of severity and the risk of harmfulness on the inspected pipeline.Voir les détails

Mots clés : Pipeline inspection, endoscopy, video processing, video segmentation, corrosion.


A.REDDAF, F. Djerfaf, K.FERROUDJI, M. Boudjerda, K. Hamdi Chérif, I. BOUCHACHI (2018)
Article de conférence

In this study, an extraction procedure using a ground coplanar wave guide GCPWG structure [1] is proposed to obtain the complex permittivity and permeability of unknown thins film materials. The method is validated for unknown materials that having the following properties: dielectric material losses, dielectric material with losses, and magneto-dielectric material with loss, deposited on a substrate with known dielectric properties. Full wave electromagnetic simulations are used to obtain the S parameters of the structure as a function of frequency [2], which are used with conformal mapping technique CMT [3] to calculate the complex permittivity and permittivity of the unknown materials. Results shown that, the parameters of the material extracted are accurate with an error of 5% for the real and imaginary values, in comparison with the values specified on [1GHz 3 GHz]. This proposed method, will prove to be useful for measuring the complex permittivity and permeability of thin films for a wide range of applications.Voir les détails

Mots clés : GCPWG structures, thin film, dielectric material, CMT

Fast Adapting Mixture Parameters Schemes forProbability Density Di erence-based DeformableModel

Aicha Baya Goumeidane, Nafaa Nacereddine (2018)
Article de conférence

This paper presents a new region-driven active contour usingthe pdf di erence to evolve. The pdf estimation is done via a new andfast Gaussian mixture model (GMM) parameters updating scheme. Theexperiments performed on synthetic and X-ray images have shown notonly an accurate contour delineation but also outstanding performancein terms of execution speed compared to the GMM estimation based onEM algorithm and to non-parametric pdf estimations.Voir les détails

Mots clés : Active contour, Adaptive mixture, GMM parameters up-dates.

Spatially Varying Weighting Function-BasedGlobal and Local Statistical Active Contours.Application to X-Ray Images

Aicha Baya Goumeidane, Nafaa Nacereddine (2016)
Article de journal

Image segmentation is a crucial task in the image processingfield. This paper presents a new region-based active contour whichhandles global information as well as local one, both based on the pixelsintensities. The trade-off between these information is achieved by aspatially varying function computed for each contour node location. Theapplication preliminary results of this method on computed tomographyand X-ray images show outstanding and efficient object extractionVoir les détails

Mots clés : image segmentation, Active contour, Averaged Shifted, histogram, pressure forces, statistics, Spatially varying function