Electronique
Hybrid Wavelet-Fractal Image Coder Applied to Radiographic Images of Weld Defects
Fractal image compression has the advantage in term of its ability to provide a very high compression ratio. Discrete wavelet transform (DWT) retains frequency as well as spatial information of the signal. These structural advantages of the DWT schemes can lead to better visual quality for compression at low bitrate. In order to combine the advantages of wavelet and fractal coding, many coding schemes incorporating fractal compression and wavelet transform have been developed. In this work we evaluate a hybrid wavelet-fractal coder for image compression, and we test its ability to compress radiographic images of weld defects. A comparative study between the hybrid wavelet-fractal coder and pure fractal compression technique have been made in order to investigate the compression ratio and corresponding quality of the image using peak signal to noise ratio. Voir les détails
Mots clés : Fractal Compression, Discrete wavelet transform, Hybrid Wavelet-Fractal Image Coder, Radiographic image
DETECTION OF DEFECTS IN WELD RADIOGRAPHIC IMAGES BY USING MULTI-SCALE GVF B-SPLINE SNAKE
In this paper, we use the active contour models (Snakes) for edge detection and segmentation of weld defects in radiographic images. Gradient Vector Flow snakes enhance the concave object extraction capability. However, the GVF snakes are sensitive to noise. Several new snake models were developed by combining different methods with GVF snake. Here, a multiscale GVF and B-spline model is proposed to overcome the traditional GVF disadvantage. Experiments on synthetic and radiographic images are promising. Voir les détails
Mots clés : Defect extraction, Snake, GVF, Multi-scale, B-Spline.
Detection of Defects in Weld Radiographic Images by Using Chan-Vese Model and Level Set Formulation
In this paper, we propose a model for active contours to detect boundaries’ objects in given image. The curve evolution is based on Chan-Vese model implemented via variational level set formulation. The particularity of this model is the capacity to detect boundaries’ objects without need to use gradient of the image, this propriety gives its several advantages: it allows detecting both contours with or without gradient, it has ability to detect automatically interior contours, and it is robust in the presence of noise. For increasing the performance of model, we introduce the level sets function to describe the active contour, the more important advantage to use level set is the ability to change topology. Experiments on synthetic and real (weld radiographic) images show both efficiency and accuracy of implemented model. Voir les détails
Mots clés : image segmentation, Curve evolution, Chan-Vese Model, EDPs
Invariant shape features and Relevance Feedback for Weld Defect Image Retrieval
Relevance feedback mechanism is used in Content-based Image Retrieval (CBIR) to attempt to minimize the amount of interaction between the user and the system required to improve the retrieval system performance. In this work, 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. Similarity measures use feature vectors based on shape descriptors invariant to usual geometric transformations. Experiments over the tested database demonstrate that the CBIR gives good results and is practical and promising for the future of welded joint radiographic examination. Voir les détails
Mots clés : weld defect, Radiographic testing, shape descriptor, CBIR, relevance feedback
Dual-Frequency Behavior of Stacked High Tc Superconducting Microstrip Patches
The dual-frequency behavior of stacked high Tc superconducting rectangular microstrip patches fabricated on a two-layered substrate is investigated using a full-wave spectral analysis in conjunction with the complex resistive boundary condition. Using a matrix representation of each layer, the dyadic Green's functions of the problem are efficiently determined in the vector Fourier transform domain. The stationary phase method is used for computing the radiation electric field of the antenna. The proposed approach is validated by comparison of the computed results with previously published data. Variations of the lower and upper resonant frequencies, bandwidth and quality factor with the operating temperature are given. Results showing the effects of the bottom patch thickness as well as the top patch thickness on the dual-frequency behavior of the stacked configuration are also presented and discussed. Finally, for a better comprehension of the dual-frequency operation, a comparison between the characteristics of the lower and upper resonances is given Voir les détails
Mots clés : Dual-frequency operation, Stacked patches, Superconducting microstrip patches
Application of Hybrid Wavelet-Fractal Compression Algorithm for Radiographic Images of Weld Defects
Based on the standard fractal transformation in spatial domain, simple relations may be found relating coefficients in detail subbands in the wavelet domain. In this work we evaluate a hybrid wavelet-fractal image coder, and we test its ability to compress radiographic images of weld defects. A comparative study between the hybrid coder and standard fractal compression technique have been made in order to investigate the compression ratio and corresponding quality of the image using peak signal to noise ratio. Numerical experiments using radiographic images of weld defects illustrate the superior performance of the hybrid coder compared to standard fractal algorithm. Voir les détails
Mots clés : Fractal Compression, Discrete wavelet transform, Wavelet-Fractal coder, Radiographic images of weld defects, Compression ratio, Peak signal to noise ratio
A Region-Based Model and Binary Level Set Function Applied to Weld Defects Detection in Radiographic Images
In this paper, we propose a model for active contours to detect boundaries’ objects in given image. The curve evolution is based on Chan-Vese model implemented via binary variational level set formulation. The particularity of this model is the capacity to detect boundaries’ objects without need to use gradient of the image, this property gives its several advantages: it allows detecting both contours with or without gradient, it has ability to detect automatically interior contours, and it is robust in the presence of noise. For increasing the performance of model, we introduce the level sets function to describe the active contour, the more important advantage to use level set is the ability to change topology. Experiments on synthetic and real (weld radiographic) images show both efficiency and accuracy of implemented model. Voir les détails
Mots clés : image segmentation, Curve evolution, Chan-Vese Model, EDPs, Level set, radiographic images
Maximum Likelihood Curves for Multiple Objects Extraction: Application to Radiographic Inspection for Weld Defects Detection
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
Adaptive and Statistical Polygonal Curve forMultiple Weld Defects Detection inRadiographic Images
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
Combined use of principal component analysis and self organization map for condition monitoring in pickling process
Process monitoring using multivariate statistical process control (MSPC) has attracted large industries types due to its practical importance and application. In this paper, a combined use of principal component analysis (PCA) and self organization map (SOM) algorithms are considered. Habitually PCA method uses T2 Hoteling's and squared predicted error (SPE) as indexes to classify processes variability. In this paper, new version of indexes called metric distances obtained from the self organization map (SOM) algorithm replace the conventional indexes proper to PCA. A comparative study between SOM, the conventional PCA and the hybrid form of PCA–SOM is examined. Application is made on the real data obtained from a pickling process. As shown in different figures, the combined approach remains important comparatively to PCA but not more than SOM. Voir les détails
Mots clés : component analysis, Condition monitoring