Electronique

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Thresholding Techniques and their Performance Evaluation for Weld Defect Detection in Radiographic Testing

N. Nacereddine, L. Hamami, D. Ziou  (2006)
Publication

In nondestructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of thresholding methods based on gray level histogram, 2D histogram and locally adaptive approach for weld defect detection in radiographic images. Voir les détails

Mots clés : 1D and 2D histogram, locally adaptive approach, performance criteria, Radiographic image, thresholding, weld defect

Probabilistic deformable models for weld defect contour estimation in radiography

N. Nacereddine, L. Hamami, D. Ziou, M. Tridi  (2006)
Publication

This paper describes a novel method for segmentation of weld defect in radiographic images. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. Our approach can be described as a region-based maximum likelihood formulation of parametric deformable contours. This formulation provides robustness against the poor image quality, and allows simultaneous estimation of the contour parameters together with other parameters of the model. Implementation is performed by a deterministic iterative algorithm with minimal user intervention. Results testify very good performance of such contour estimation approach. Voir les détails

Mots clés : Gaussian and Rayleigh distributions, contour estimation, maximum likelihood, parametric deformable contours

Robustness of Radon transform to white additive noise: General case study

N. Nacereddine, S. Tabbone, D. Ziou  (2014)
Publication

A detailed study is presented on the robustness of the Radon transform to additive white noise for the general case of a rectangular grey-level image. Voir les détails

Mots clés : radon transform, white additive noise

Object recognition using Radon transform based RST parameter estimation

N. Nacereddine, S. Tabbone, D. Ziou  (2012)
Publication

In this paper, we propose a practical parameter recovering approach, for similarity geometric transformations using only the Radon transform and its extended version on [0, 2π]. The derived objective function is exploited as a similarity measure to perform an object recognition system. Comparison results with common and powerful shape descriptors testify the effectiveness of the proposed method in recognizing binary images, RST transformed, distorted, occluded or noised. Voir les détails

Mots clés : RST parameters, radon transform, object recognition

Similarity Transformation Parameters Recovery based on Radon Transform. Application in Image Registration and Object Recognition

N. Nacereddine, S. Tabbone, D. Ziou  (2015)
Publication

The Radon transform, since its introduction in the beginning of the last century, has been studied deeply and applied by researchers in a great number of applications, especially in the biomedical imaging fields. By using the Radon transform properties, the issue is to recover the transformation parameters regarding the rotation, scaling and translation, by handling only the image projections assuming no access to the spatial domain of the image. This paper proposes an algorithm using an extended version of the Radon transform to recover such parameters relating to two unknown images, directly from their projection data. Especially, our approach deals with the problem of the estimation accuracy of the rotation angle and its finding in one step instead of two steps as it is reported in the literature. This method may be applied in image registration as well in object recognition. The results are, for the first time, exploited in object recognition where comparison with powerful descriptors shows the outstanding performance of the proposed paradigm. Moreover, the influence of additive noise on registration and recognition experiments is discussed and shows the efficiency of the method to reduce the effect of the noise. Voir les détails

Mots clés : radon transform, 2π-Based Radon transform, Rotation, Scaling and translation transforms, Parameters recovery algorithm, Additive image noise

Maximum Likelihood Curves for Multiple Objects Extraction: Application to Radiographic Inspection for Weld Defects Detection

A. B. Goumeidane, M. Khamadja, N. Nacereddine  (2011)
Publication

This paper presents an adaptive probabilistic region-based deformable model using an explicit representation that aims to extract automatically defects from a radiographic film. To deal with the height computation cost of such model, an adaptive polygonal representation is used and the search space for the greedy-based model evolution is reduced. Furthermore, we adapt this explicit model to handle topological changes in presence of multiple defects. Voir les détails

Mots clés : Explicit deformable model, adaptive contour representation, Maximum likelihood criterion

Bayesian Pressure Snake forWeld Defect Detection

A. B. Goumeidane, M. Khamadja, N. Nacereddine  (2009)
Publication

Image Segmentation plays a key role in automatic weld defect detectionand classification in radiographic testing. Among the segmentation methods,boundary extraction based on deformable models is a powerful techniqueto describe the shape and then deduce after the analysis stage, the type of thedefect under investigation. This paper describes a method for automatic estimationof the contours of weld defect in radiographic images. The method uses astatistical formulation of contour estimation by exploiting statistical pressuresnake based on non-parametric modeling of the image. Here the edge energy isreplaced by a region energy which is a function of statistical characteristics ofarea of interest. Voir les détails

Mots clés : Snake, images segmentation, pdf estimation, radiographic images, Non Destructive Inspection

Adaptive and Statistical Polygonal Curve forMultiple Weld Defects Detection inRadiographic Images

A. B. Goumeidane, M. Khamadja, N. Nacereddine  (2011)
Publication

With the advances in computer science and artificial intelligencetechniques, the opportunity to develop computer aided techniquefor radiographic inspection in Non Destructive Testing arose. This paperpresents an adaptive probabilistic region-based deformable model usingan explicit representation that aims to extract automatically defects froma radiographic film. To deal with the height computation cost of suchmodel, an adaptive polygonal representation is used and the search spacefor the greedy-based model evolution is reduced. Furthermore, we adaptthis explicit model to handle topological changes in presence of multipledefects. Voir les détails

Mots clés : Radiographic inspection, Explicit deformable model, adaptive contour representation, Maximum likelihood criterion, Multiple contours

Local and Global Statistics-Based Explicit Active Contour for Weld Defect Extraction in Radiographic Inspection

A. B. Goumeidane, N. Nacereddine, M. Khamadja  (2013)
Publication

Welding is a process of utmost importance in the metal industry. With the advances in computer science and artificial intelligence techniques, the opportunity to develop computer aided technique for radiographic inspection in Non Destructive Testing arose. This paper deals with the weld defects detection in radiographic films. A greedy active contour model is used exploiting global and local statistics to drive the model to the boundaries. Moreover, and to decrease the computation cost, the local statistics computation is done only for pixels in a selected band. Results seem to be promising ones. Voir les détails

Mots clés : Radiographic inspection, weld defects, Active contours

Adaptive B-Spline Model Based Probabilistic Active Contour for Weld Defect Detection in Radiographic Imaging

N. Nacereddine, L. Hamami, D. Ziou, A. B. Goumeidane  (2010)
Publication

This paper describes a probabilistic region-based deformable model using a new adaptive scheme for B-spline representation. The idea is to adapt the number of spline control points which are necessary to describe an object with complex shape. For this purpose, the curve segment length (CSL) is used as criterion. The proposed split and merge strategy on the spline model consists in: adding a new control point when CSL is greater than a certain splitting threshold so that the contour tracks all the concavities and, removing a control point when CSL is less to a certain merging threshold so that the contour aspect maintains its smoothness. Noise on synthetic and real weld radiographic images is assumed following Gaussian or Rayleigh distribution. The experiments carried out confirm the adequacy of this approach, especially in tracking pronounced concavities contained in images. Voir les détails

Mots clés : weld defect, Active contour, adaptive B-spline, split and merge operations