Electronique
Video Processing and Analysisfor Endoscopy-Based InternalPipeline Inspection
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
Asymmetric Generalized Gaussian DistributionParameters Estimation based on MaximumLikelihood, Moments and Entropy
In this paper, we address the problem of estimatingthe parameters of Asymmetric Generalized Gaussian Distribution(AGGD) using three estimation mehods, namely, Maximum LikelihoodEstimation (MLE), Moment Matching Estimation (MME)and Entropy Matching Estimaion (EME). For this purpose, thesemethods are applied on an unimodal histogram fitting of animage corrupted with AGGD noise. Experiments show that theeffectiveness of each method comparatively to the other onedepends on the variation range of the shape factor. Voir les détails
Mots clés : Asymmetric generalized Gaussian distribution, Parameter estimation, maximum likelihood, Moments, Entropy.
A novel correlation filter based on variational calculus
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
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
Tool combination for the description of steel surface image and defect classification
In industry, the automatic recognition of surface defects of flat steel products still represents a real challenge. Indeed, in addition to constraints such as the image noise or blur, there is neither an agreed standard of these defects nor a standard method that can ensure the defect identification, whatever are their size, shape, orientation and location. Thus, the complexity of the algorithm that deals with this matter always depends on specific needs of the application. In this paper, we give details on an approach that combines Gabor wavelets (GW) and the local phase quantization technique (LPQ), to describe the steel surface images, and uses the histogram to extract their characteristics. The defect classification is carried out by means of two classifiers, namely the nearest neighbors and the support vector machine. The method assessment is based on testing different parameter values of the used tools. The approach shows a good performance in terms of recognition rates and feature vector length, which impacts the computing time. Also, the study reveals its suitability for an online steel surface defect recognition application. Voir les détails
Mots clés : Quality control, Computer vision, metal surface imaging, Filter bank application, pattern analysis and recognition
Surface Flaw Classification Based on Dual Cross Pattern
The evaluation of flat steel surface quality is mainly concerned with detecting and identifying product surface defects. Although the variety of the implemented techniques, this type of control still presents a challenge. In this paper, we assess the Dual Cross Pattern technique, as a feature descriptor, that should be quite discriminative, to ease the steel surface defect classification. The histograms extracted from the captured DCP features are concatenated to represent the global image feature vector. The procedure parameters, as the DCP circle radius, the number of the training images and their choice, are considered to show their impact on the results. The experiment conducted on the NEU published defect database shows that, compared to the other used techniques, the proposed approach reveals not only interesting recognition rates but presents advantages in time coast too. Voir les détails
Mots clés : Image description, pattern recognition, Product quality, steel surface defects, hot rolling process
Steel Strip Surface Defect Identi?cation using MultiresolutionBinarized Image Features
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
Improved cross pattern approach for steel surface defect recognition
In steel-making processes, different methods are used for online surface product monitoring. Such a control has become anecessity to avoid additional costs resulting from the poor quality of the final product. With the reported performance that variesfrom one application to another, all the applied methods have to meet a minimum of criteria as accuracy and speed. Thiseffectiveness is assured thanks to a relevant image description and efficient defect classification algorithms. The Dual CrossPattern technique, successfully applied in face recognition, is a concept that relies on coding pixels to provide such a discriminatingdescription of the image. Its principle can perfectly be used in industrial vision applications for surface defect recognition.In this study, the relevance of this method of describing defect images is evaluated, and improvements are proposed to increase itsefficiency. The experimental study shows that the pixel coding that considers the variations of the intensity in several directionsand captures the information from more than one pixel-neighborhood level makes it possible to better detect the variability in thedefect image and helps to increase the defect recognition rate. The experiments are carried out with the use of the publishedNortheastern University (NEU) database for the comparison and with a new constructed database to better show the improvementsbrought by the proposed approach. Voir les détails
Mots clés : Computer vision, Image description, Surface defect classification, Steel process
Modeling and Simulation of Biaxial Strained P-MOSFETs: Application to a Single and Dual Channel Heterostructure
The objectives of this work are focused on the application of strained silicon on MOSFET transistor. To do this, the impact and benefits obtained with the use of strained silicon technology on p-channel MOSFETs are presented. This research attempt to create conventional and two-strained silicon MOSFETsfabricated from the use of TCAD, which is a simulation tool from Silvaco. In our research, two-dimensional simulation of conventional MOSFET, biaxial strained PMOSFET, and dual-channel strained P-MOSFET has been achieved to extract their characteristics. ATHENA and ATLAS have been used to simulate the process and validate the electronic characteristics. Our results allow showing improvements obtained by comparing the three structures and their characteristics. The maximum of carrier mobility improvement is achieved with the percentage of 35.29 % and 70.59 % respectively, by result an improvement in drive current with the percentage of 36.54 % and 236.71 %, and reduction of leakage current with the percentage of 59.45 % and 82.75 %, the threshold voltage is also enhanced with the percentage of 60 % and 61.4%. Our simulation results highlight the importance of incorporating strain technology MOSFET transistors. Voir les détails
Mots clés : Biaxial strain, CMOS technology, SILVACO-TCAD, Strained silicon layers
The importance of using dual-channel heterostructure in strained P-MOSFETs
We present in this work a dual-channel heterostructure strained structure, introduce the high carrier mobility Awaited in heterostructure devices while using several models which are: CVT, SHIRAHATA, and WATT, we present a two-dimensional simulation of dual strained channel heterostructure P-MOSFETs. This study is accomplished usingSILVACO-TCAD simulation software, the comparison of the effect of using strain technique on P-MOSFET transistors will demonstrate the importance of using strain technique especially in dual channel heterostructure MOSFET. The simulation of fabrication steps and the extraction of the electronic proprieties in terms of transfer and output characteristics, transconductance, and the quasi-static capacitance allow understanding and interpreting these enhancements Voir les détails
Mots clés : Strained Silicon, SiGe layer, MOSFET; Heterostructure, simulation, Silvaco