Liste des publications

Nombre total de résultats : 572
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Unsupervised weld defect classification in radiographic images usingmultivariate generalized Gaussian mixture model with exactcomputation of mean and shape parameters

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou  (2019)
Publication

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 themanufacturing process in order to prevent unfair harm to the industrial plant in construction. For thispurpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographictesting is presented, where a succession of operations of preprocessing, image segmentation, featureextraction andfinally defects classification is carried out on radiographic images. The last operationwhich is the main contribution in this paper consists in an unsupervised classifier based on afinitemixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newlyapplied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGDbasedmixture model are estimated using the Expectation-Maximization algorithm where, exactcomputations of mean and shape parameters are originally provided. The weld defect database representfour weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by ashape geometric descriptor composed of geometric measures. An outstanding performance of theproposed 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%. Theefficiency of the proposed classifier is mainly due to theflexiblefitting of the input data, thanks to theMGGD shape parameter. Voir les détails

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

Video Processing and Analysisfor Endoscopy-Based InternalPipeline Inspection

Nafaa Nacereddine, Aissa Boulmerka, Nadia MHAMDA  (2019)
Publication

Because of the increasing requirements in regards to the pipeline transport regulations, the operators take care to the rigorous application of checking routines that ensure nonoccurrence of leaks and failures. In situ pipe inspection systems such as endoscopy, remains a reliable mean to diagnose possible abnormalities in the interior of a pipe such as corrosion. Through digital video processing, the acquired videos and images are analyzed and interpreted to detect the damaged and the risky pipeline areas. Thus, the objective of this work is to bring a powerful analysis tool for a rigorous pipeline inspection through the implementation of specific algorithms dedicated to this application for a precise delimitation of the defective zones and a reliable interpretation of the defect implicated, in spite of the drastic conditions inherent to the evolution of the endoscope inside the pipeline and the quality of the acquired images and videos. Voir les détails

Mots clés : video processing, endoscopy, Pipeline inspection

A novel correlation filter based on variational calculus

Djemel Ziou, Dayron Rizo Rodriguez, Nafaa Nacereddine, Salvatore Tabbone  (2019)
Publication

Correlation filters have been a popular technique for tackling image classification problems. The traditionalcriteria used to design correlation filters overlook some properties that can improve their discriminative power.Therefore, new criteria are proposed to design a novel correlation filter. Such criteria take advantage ofnegative samples, spatial information and the smoothness of the correlation output space. A closed formis derived from the criteria proposed using variational calculus. Moreover, it is shown that the resultingcorrelation filter is a bandpass filter. Experiments are conducted for face identification under illuminationvariation for a single training image per subject and head pose classification. The correlation filter proposeddelivers favorable scores when compared to other correlation filters and state-of-the-art approaches Voir les détails

Mots clés : Correlation filter, Variational calculus, Face identification, Illumination variation, Single training image, Pose classification

Scale space Radon transform

Djemel Ziou, Nafaa Nacereddine, Aicha Baya Goumeidane  (2021)
Publication

An extension of Radon transform by using a measure function capturing the user need isproposed. The new transform, called scale space Radon transform, is devoted to the casewhere the embedded shape in the image is not ?liform. A case study is brought on a straightline and an ellipse where the SSRT behaviour in the scale space and in the presence of noiseis deeply analyzed. In order to show the effectiveness of the proposed transform, the exper-iments have been carried out, ?rst, on linear and elliptical structures generated syntheticallysubjected to strong altering conditions such blur and noise and then on structures imagesissued from real-world applications such as road traf?c, satellite imagery and weld X-rayimaging. Comparisons in terms of detection accuracy and computational time with well-known transforms and recent work dedicated to this purpose are conducted, where theproposed transform shows an outstanding performance in detecting the above-mentionedstructures and targeting accurately their spatial locations even in low-quality images. Voir les détails

Mots clés : radon transform, line, ellipse, scale space, noise

Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model

Rabah ABDELKADER, Naim Ramou, Mohammed Khorchef, Nabil CHETIH, Yamina BOUTICHE  (2021)
Publication

The welding defects detection in industries is becoming an important area and is attracting the attention of many researchers. Radiography is one of the most widely used techniques for inspecting weld defects. X-ray images are generally characterized by low contrast, poor quality and uneven illumination, so the extraction of weld defects could become a difficult task. Among the techniques most used in this field, it is the active contour and the main problem of this technique is the initial contour selection. To solve this problem and obtain reliable and efficient detection of welding defects, we propose in this work a new approach for welding defects detection from x-ray image based on an improved Chan-Vese model. This improved model is based on three stages. The first stage is the detection the region of interest. In the second stage, we apply the Fuzzy C-Mean (FCM) algorithm to select one of the clusters as the initial contour. In the third stage, we use the Chan-Vese model and the selected initial contour to segment the acquired images and obtain the boundaries of the weld defects. Experiments are carried out on different x-ray welding images of the GDxray database in order to extract the characteristics of the welding defects. The results obtained show the effectiveness of the proposed approach compared to conventional techniques. Voir les détails

Mots clés : Chan-Vese model Fuzzy, C-means clustering, X-ray image, Welding defects

Tamanrasset’s Clay Characterization and Use as Low Cost, Ecofriendlyand Sustainable Material for Water Treatment: Progress and Challengein Copper Cu (II)

Aicha Kourim1, Moulay Abderrahmane Malouki2, Aicha Ziouche3, Mouna Boulahbal4, d and Madjda Mokhtari5  (2021)
Publication

In this study, the adsorption of copper Cu (II) from aqueous solution, on Tamanrasset’sclay which is low cost adsorbent, was studied using batch experiments. The adsorption study includesboth equilibrium adsorption isotherms and kinetics. The characterization of the adsorbent necessitatedseveral methods such as X-Ray Diffraction, Scanning Electron Microscopy coupled with EnergyDispersive X-ray, BET for specific surface area determination, Fourier transform infraredspectroscopy and thermogravimetric analysis. Indeed, various parameters were investigated such ascontact time, initial metal ion concentration, mass of solid, pH of the solution and temperature. Theadsorption process as batch study was investigated under the previews experimental parameters. Theresults revealed that the adsorption capacity of Cu2+ is maximized at naturel pH of metal 5.5. Removalof copper by the clay of Tamanrasset (kaolinite) achieved equilibrium within 50 minutes; the resultsobtained were found to be fitted by the pseudo-second order kinetics model. The equilibrium processwas well described by the Langmuir model and the maximum adsorption capacity was found to be26.59 mg/g. Voir les détails

Mots clés : adsorption, Clay, copper, kinetic, Isotherms

First principal investigation of structural, morphological, optoelectronic and magnetic characteristics of sprayed Zn: Fe2O3 thin films

Rihab BenAyed, MejdaAjili, Jorge M.Garcia, AichaZiouche, Jose Luis CostaKramer, Najoua KamounTurki  (2020)
Publication

Undoped and Zn-doped Fe2O3 thin films were grown through spray pyrolysis. Zinc doping effect on the physical properties was investigated in detail. X-ray diffraction analysis confirms that all the Fe2O3 thin films showed a rhombohedral structure. The surface morphological study shows an interesting dendrite structure. The estimated band gaps energies were increased from 2.13 to 2.21 eV for indirect transition and from 1.80 to 1.85 eV for direct transition as function of doping ratio which was increased from 2 to 8 at. % Zn. The resistivity value (ρ) of un-doped Fe2O3 thin film is 6.06 × 104 Ω. cm and as adding Zn ions, ρ consequently decreased to 52 Ω. cm for 6 at. % Zn-doped Fe2O3 thin films. Vibrating sample magnetometer (VSM) measurements showed an increase of the saturation magnetization with the Zn2+ insertion. Further, a ferromagnetic behavior was observed. Voir les détails

Mots clés : Ferromagnetic, semiconductor, Fe2O3, Zinc doping, Low resistivity

Unraveling the effect of Bi2S3 on the optical, electrical and magnetic properties of γ-MnS-based composite thin films

Z.Amara, M.Khadraoui, R.Miloua, A.Boukhachem, A.ZIOUCHE  (2020)
Publication

(Bi2S3)x (γ-MnS)1-x composite thin films have been deposited onto glass substrates using spray pyrolysis method. The structural and compositional investigations confirmed the co-existence of Bi2S3 and γ-MnS binary compounds in the thin films. The surface morphology indicated that the increase in Bi2S3 concentration influences both the shape and the size of γ-MnS crystallites. The optical analysis via transmittance and reflectance measurements revealed that the band gap energy Eg decreased from 3.29 eV to 1.5 eV in terms of Bi2S3 content. The electrical parameters such as resistivity ρ, mobility μ, carrier concentrations and Hall coefficient have been obtained by Hall Effect measurements. It is found than incorporation of Bi2S3 enhances the conductivity, and p-type conduction of γ-MnS could be converted to n-type at x = 0.5. The vibrating sample magnetometer measurement has revealed that (Bi2S3)x (γ-MnS)1-x composite thin films have a ferromagnetic behavior at room temperature. Voir les détails

Mots clés : γ-MnS, Bi2S3, Spray pyrolysis, Magnetic Properties

Investigation of some physical properties of pure and Co-doped MoO3 synthesized on glass substrates by the spray pyrolysis method

N.Benameur, M.A.Chakhoum, A.Boukhachem, M.A.Dahamni, A.ZIOUCHE  (2019)
Publication

Pristine and Cobalt (Co)-doped MoO3 nanofilms were synthesized on glass substrates using the spray pyrolysis method. The nanometric pristine MoO3 films were prepared from the 10−2 M.L-1 solution of ammonium molybdate tetrahydrate [(NH4)6Mo7O24,4H2O] in distilled water. Co-doping at 0.5, 0.75 and 1% was achieved by adding cobalt (II) chloride hexahydrate (Cl2CoH12O6) in the pristine solution. The structure and the morphology of the films were investigated by means of X-ray diffraction and atomic force microscopy: two pronounced (020) and (040) peaks corresponding to the orthorhombic structure phase of α-MoO3 were detected. The AFM observations revealed the formation of micro-plates, parallel to the surface plane, with a roughness ranging from 33?nm to 54?nm. Optical properties were investigated through reflectance, transmittance and photoluminescence measurements. The optical band gap, the Urbach energy and the refractive index were deduced from these measurements. The presence of oxygen vacancies was revealed from the interband transitions in the blue and green domains. Co-doped MoO3 nanofilms showed ferromagnetic behavior. The photocatalytic degradation of an aqueous solution of methylene blue (MB) under UV irradiation, in the presence of Co-MoO3 nanomfilms, has been carried out using UV–vis spectrometery: the intensity of the absorption peak recorded at 660?nm decreased with the increase of the UV-illumination time while the color of the initial MB solution was drastically waned. Voir les détails

Mots clés : Spray pyrolysis method, MoO3 nanofilms, optical properties, Magnetic Properties

Steel Strip Surface Defect Identi?cation using MultiresolutionBinarized Image Features

Zoheir MENTOURI, Abdelkrim Moussaou, Djalil BOUDJEHEM, Hakim DOGHMANE  (2020)
Publication

The shaped steel strip, in the hot rolling process,may exhibit some surface ?aws. Their origin could bethe internal discontinuities in the input product or thethermomechanical transformation of the material, duringthe shaping process. Such defects are of a random occurrenceand may lead to costly rework operations or to adowngrading of the ?nal product. So, they should bedetected and identi?ed as soon as possible, to allow atimely decision-making. For such a quality monitoring, theused vision systems are mainly based on an imagedescription and a reliable classi?cation. In this paper, weexplore pre-de?ned image ?lters and work on a procedureto extract a discriminant image feature, while realizing thebest trade-off between the improved recognition rate of thesurface defects and the computing time. The proposedmethod is a multiresolution approach, based on theBinarized Statistical Image Features method, employed todate in biometrics. The ?lters, pre-learnt from naturalimages, are applied to steel defect images as a new surfacestructure indicator. They provide a quite discriminating image description. A relevant data reduction is used togetherwith a classi?er to allow an ef?cient recognition rate ofthe defective hot rolled products. Voir les détails

Mots clés : Computer vision, statistical features, Classi?cation, strip surface defects, hot rolling process