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Nafaa Nacereddine - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised Weld Defect classification in radiographic images using multivariate generalized gaussian mixture model with exact computation of mean and shape parameters
    Computers in Industry, 2019
    Co-Authors: Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou
    Abstract:

    Abstract 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.

  • gentle adaboost algorithm for Weld Defect classification
    Signal Processing: Algorithms Architectures Arrangements and Applications, 2017
    Co-Authors: F Mekhalfa, Nafaa Nacereddine
    Abstract:

    In this paper, we present a new strategy for automatic classification of Weld Defects in radiographs based on Gentle Adaboost algorithm. Radiographic images were segmented and moment-based features were extracted and given as input to Gentle Adaboost classifier. The performance of our classification system is evaluated using hundreds of radiographic images. The classifier is trained to classify each Defect pattern into one of four classes: Crack, Lack of penetration, Porosity, and Solid inclusion. The experimental results show that the Gentle Adaboost classifier is an efficient automatic Weld Defect classification algorithm and can achieve high accuracy and is faster than support vector machine (SVM) algorithm, for the tested data.

  • SPA - Gentle Adaboost algorithm for Weld Defect classification
    2017 Signal Processing: Algorithms Architectures Arrangements and Applications (SPA), 2017
    Co-Authors: F Mekhalfa, Nafaa Nacereddine
    Abstract:

    In this paper, we present a new strategy for automatic classification of Weld Defects in radiographs based on Gentle Adaboost algorithm. Radiographic images were segmented and moment-based features were extracted and given as input to Gentle Adaboost classifier. The performance of our classification system is evaluated using hundreds of radiographic images. The classifier is trained to classify each Defect pattern into one of four classes: Crack, Lack of penetration, Porosity, and Solid inclusion. The experimental results show that the Gentle Adaboost classifier is an efficient automatic Weld Defect classification algorithm and can achieve high accuracy and is faster than support vector machine (SVM) algorithm, for the tested data.

  • hybrid shape descriptors for an improved Weld Defect retrieval in radiographic testing
    Soft Computing, 2015
    Co-Authors: Nafaa Nacereddine, Djemel Ziou
    Abstract:

    In this paper, four region-based shape descriptors well reported in the literature are used to characterize Weld Defect types of crack, lack of penetration, porosity and solid inclusion, usually encountered in radiographic testing of Welds. The rectangularity and the roundness in the geometric descriptor (GEO) are used in order to propose an hybridization algorithm so that the hybrid descriptor issued from GEO and each of the other descriptors becomes more discriminant in such application where, due to bad radiographic image quality and Weld Defect typology, the human film interpretation is often inconsistent and labor intensive. According to the results given in the experiments, the efficiency of the proposed hybrid descriptors is confirmed on the Weld Defects mentioned above where, the retrieval scores are significantly improved compared to the original descriptors used separately.

  • computer aided Weld Defect delineation using statistical parametric active contours in radiographic inspection
    Journal of X-ray Science and Technology, 2015
    Co-Authors: Aicha Baya Goumeidane, Nafaa Nacereddine, Mohammed Khamadja
    Abstract:

    A perfect knowledge of a Defect shape is determinant for the analysis step in automatic radiographic inspection. Image segmentation is carried out on radiographic images and extract Defects indications. This paper deals with Weld Defect delineation in radiographic images. The proposed method is based on a new statistics-based explicit active contour. An association of local and global modeling of the image pixels intensities is used to push the model to the desired boundaries. Furthermore, other strategies are proposed to accelerate its evolution and make the convergence speed depending only on the Defect size as selecting a band around the active contour curve. The experimental results are very promising, since experiments on synthetic and radiographic images show the ability of the proposed model to extract a piece-wise homogenous object from very inhomogeneous background, even in a bad quality image.

Jiluan Pan - One of the best experts on this subject based on the ideXlab platform.

  • automatic Weld Defect detection method based on kalman filtering for real time radiographic inspection of spiral pipe
    Ndt & E International, 2015
    Co-Authors: Yirong Zou, Baohua Chang, Jiluan Pan
    Abstract:

    Abstract A method based on Kalman filtering is proposed for Weld Defect detection in real-time radiographic NDT of spiral pipes. The existence of the image noises and the inhomogeneity of the background contrast induce numerous false alarms. In this paper, the trajectory continuity of the Defects in the image sequence is detected by Kalman filtering for the identification of true Defects. Potential Defect regions without continuous motion are considered as false alarms and are eliminated. Experiments are performed to demonstrate the adaptability of the proposed method. The robustness of the method is also verified under unstable detection velocity.

Han Shi - One of the best experts on this subject based on the ideXlab platform.

  • automatic Weld Defect detection based on potential Defect tracking in real time radiographic image sequence
    Ndt & E International, 2012
    Co-Authors: Jiaxin Shao, Baohua Chang, Han Shi
    Abstract:

    An effective and adaptive method is proposed to automatically detect Weld Defects using Defect tracking in real-time radiographic image sequence of a moving Weld. Firstly, a Defect segmentation algorithm with low threshold is used to segment all of the potential Weld Defects in each image of the sequence. Then the modified Hough transform is employed to track the center of gravity of potential Defects in image sequence, and the potential Defects that cannot be tracked are eliminated as false Defects. Experiment results show that the proposed method can detect Weld Defects with high certainty and avoid false alarms caused by the noise.

  • automatic Weld Defect detection in real time x ray images based on support vector machine
    International Congress on Image and Signal Processing, 2011
    Co-Authors: Jiaxin Shao, Han Shi, Li Wang, Huayong Cao
    Abstract:

    Automatic Weld Defect detection based on real-time X-ray image plays a vital role in improving the automation level of radiographic inspection in industry. Most of the existing real-time automatic inspection technologies only use Defect segmentation algorithms, which leads to the difficulty of reducing both the undetected rate and false alarm rate. In this paper, an effective method based on Support Vector Machine (SVM) is proposed to detect Weld Defect in real-time X-ray images. Firstly, all potential Defects are segmented by background subtraction algorithm. Then three features including Defect area, average grayscale difference to its surrounding district and grayscale standard deviation are extracted. Lastly, the extracted features are used as input to SVM classifier to distinguish non-Defects from Defects. Results show that the proposed automatic Defect detection method can reduce the undetected rate and false alarm rate effectively in real-time X-ray images of Weld.

Baohua Chang - One of the best experts on this subject based on the ideXlab platform.

  • automatic Weld Defect detection method based on kalman filtering for real time radiographic inspection of spiral pipe
    Ndt & E International, 2015
    Co-Authors: Yirong Zou, Baohua Chang, Jiluan Pan
    Abstract:

    Abstract A method based on Kalman filtering is proposed for Weld Defect detection in real-time radiographic NDT of spiral pipes. The existence of the image noises and the inhomogeneity of the background contrast induce numerous false alarms. In this paper, the trajectory continuity of the Defects in the image sequence is detected by Kalman filtering for the identification of true Defects. Potential Defect regions without continuous motion are considered as false alarms and are eliminated. Experiments are performed to demonstrate the adaptability of the proposed method. The robustness of the method is also verified under unstable detection velocity.

  • automatic Weld Defect detection based on potential Defect tracking in real time radiographic image sequence
    Ndt & E International, 2012
    Co-Authors: Jiaxin Shao, Baohua Chang, Han Shi
    Abstract:

    An effective and adaptive method is proposed to automatically detect Weld Defects using Defect tracking in real-time radiographic image sequence of a moving Weld. Firstly, a Defect segmentation algorithm with low threshold is used to segment all of the potential Weld Defects in each image of the sequence. Then the modified Hough transform is employed to track the center of gravity of potential Defects in image sequence, and the potential Defects that cannot be tracked are eliminated as false Defects. Experiment results show that the proposed method can detect Weld Defects with high certainty and avoid false alarms caused by the noise.

Djemel Ziou - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised Weld Defect classification in radiographic images using multivariate generalized gaussian mixture model with exact computation of mean and shape parameters
    Computers in Industry, 2019
    Co-Authors: Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou
    Abstract:

    Abstract 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.

  • hybrid shape descriptors for an improved Weld Defect retrieval in radiographic testing
    Soft Computing, 2015
    Co-Authors: Nafaa Nacereddine, Djemel Ziou
    Abstract:

    In this paper, four region-based shape descriptors well reported in the literature are used to characterize Weld Defect types of crack, lack of penetration, porosity and solid inclusion, usually encountered in radiographic testing of Welds. The rectangularity and the roundness in the geometric descriptor (GEO) are used in order to propose an hybridization algorithm so that the hybrid descriptor issued from GEO and each of the other descriptors becomes more discriminant in such application where, due to bad radiographic image quality and Weld Defect typology, the human film interpretation is often inconsistent and labor intensive. According to the results given in the experiments, the efficiency of the proposed hybrid descriptors is confirmed on the Weld Defects mentioned above where, the retrieval scores are significantly improved compared to the original descriptors used separately.

  • Fusion-based shape descriptor for Weld Defect radiographic image retrieval
    The International Journal of Advanced Manufacturing Technology, 2013
    Co-Authors: Nafaa Nacereddine, Djemel Ziou, Latifa Hamami
    Abstract:

    Content-based image retrieval with relevance feedback plays nowadays an important role in several machine vision applications. In this paper, a such system is proposed for Weld radiograms in radiographic testing, with the aim of searching from the overall image database, interactively with the radiograph expert, discontinuities similar to some common Weld Defect types such as crack, lack of penetration, porosity, and solid inclusion. Therefore, shape features characterizing efficiently these Defect indications are required. Two shape descriptors are proposed: a shape geometric descriptor (SGD) consisting of a set of invariant shape geometric measures chosen on the basis of their relationships with the Weld Defect classes and a generic Fourier descriptor (GFD) known for its discrimination powerfulness for planar filled objects. To improve the Weld Defect retrieval results, we propose a new fusion-based shape descriptor. The idea of the fusion strategy is to examine the compactness and the rectangularity measures in SGD and derive a criterion permitting the design of a new descriptor f(GFD,SGD) able to better discriminate, particularly, between the problematic Defect classes of crack and lack of penetration. Experiments conducted on Weld Defect image database show the strength of the proposed hybrid descriptor compared to GFD and SGD, simply or hierarchically concatenated or used separately.

  • adaptive b spline model based probabilistic active contour for Weld Defect detection in radiographic imaging
    Soft Computing, 2010
    Co-Authors: Nafaa Nacereddine, Djemel Ziou, Latifa Hamami, Aicha Baya Goumeidane
    Abstract:

    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.

  • Image Thresholding for Weld Defect Extraction in Industrial Radiographic Testing
    World Academy of Science Engineering and Technology International Journal of Computer Electrical Automation Control and Information Engineering, 2007
    Co-Authors: Nafaa Nacereddine, Latifa Hamami, Djemel Ziou
    Abstract:

    In non destructive 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, 2-D histogram and locally adaptive approach for Weld Defect extraction in radiographic images.