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Segmentation of Welding Defects using Level Set Methods

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2012年第6期7卷 1001-1008页

作者： Mohammed, Halimi Naim, Ramou Image and Signal Processing Laboratory. Welding and Control Research Center

Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the re-initialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

关键词： image segmentation Level set Weld defects Radiographic image

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Target detection and recognition in SAR imagery based on KFDA

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Journal of Systems Engineering and Electronics 2015年第4期26卷 720-731页

作者： Fei Gao Jingyuan Mei Jinping Sun Jun Wang Erfu Yang Amir Hussain School of Electronic and Information Engineering Beihang University Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde Cognitive Signal-Image and Control Processing Research Laboratory School of Natural SciencesUniversity of Stirling

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

关键词： synthetic aperture radar （SAR） target detection ker-nel fisher discriminant analysis （KFDA） target recognition imageEuclidean distance （IMED） support vector machine （SVM）.

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Optimal Multimodel Representation by Laguerre Filters Applied to a Communicating Two Tank System

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Journal of Systems Science & Complexity 2018年第3期31卷 621-646页

作者： SAMEH Adaily ABDELKADER Mbarek TAREK Garna JOSE Ragot Laboratory of Automatic Control Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia Higher Institute of Applied Science and Technology of Sousse University of Sousse 4003 Tunisia Center of Research on Automatic of Nancy CNRS 2 Avenue de la for de Haye 54516 Vandoeuvre Cedex France.

This paper presents the development of a new nonlinear representation by exploiting the multimodel approach and the new linear representation ARX-Laguerre for each operating region. The resulting multimodel, entitled ARX-Laguerre multimodel, is characterized by the parameter number reduction with a recursive representation. However, a significant reduction of this multimodel is subject to an optimal choice of Laguerre poles characterizing each local linear model ARX-Laguerre. Therefore, the authors propose an optimization algorithm to estimate, from input/output measurements, the optimal values of Laguerre poles. The ARX-Laguerre multimodel as well as the proposed optimization algorithm are tested on a continuous stirred tank reactor system （CSTR）. Moreover, the authors take into account a practical validation on an experimental communicating two tank system （CTTS）.

关键词： ARX-Laguerre model Laguerre poles multimodel approach optimization

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Extraction of weld defect from radiographic images using the level set segmentation without re-initialization

Extraction of weld defect from radiographic images using the...

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International Conference on Computer Applications Technology (ICCAT)

作者： N. Ramou M. Halimi Image and Signal Processing Laboratory Welding and Control Research Center Algiers Algeria

ISBN: (纸本)9781467352840

All level set based image segmentation methods are based on an assumption that the level set function is close to a signed distance function (SDF). Small time step and costly re-initialization procedure must be applied to guarantee this assumption, and in order to calculate the gradient, simple numerical schemes, based on finite differences, are applied. In this paper, in order to achieve higher order accuracy in the temporal discretization, we have used Total Variation Diminishing (TVD) Runge Kutta (RK) methods. The spatial derivatives are determined by using the Weighted Essentially Non-Oscillatory methods (WENO-5) that accurately capture the formation of sharp gradients in the moving fronts. In the other hand, we have used the level set method without re-initialization in order to speed up the evolutionary process. Experiments results show that we have obtained good results both on synthetic and real images.

关键词： Level set image edge detection Mathematical model Equations Shape Welding image segmentation

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Visual attention model with a novel learning strategy and its application to target detection from SAR images 1

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8th International Conference on Brain Inspired cognitive Systems, BICS 2016

作者： Gao, Fei Xue, Xiangshang Wang, Jun Sun, Jinping Hussain, Amir Yang, Erfu School of Electronic and Information Engineering Beihang University Beijing100191 China Cognitive Signal-Image and Control Processing Research Laboratory School of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom

ISBN: (数字)9783319496856

ISBN: (纸本)9783319496849

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm. © Springer International Publishing AG 2016.

关键词： Object detection

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Combining deep convolutional neural network and SVM to SAR image target recognition 10

Combining deep convolutional neural network and SVM to SAR i...

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Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017

作者： Gao, Fei Huang, Teng Wang, Jun Sun, Jinping Yang, Erfu Hussain, Amir School of Electronic and Information Engineering Beihang University Beijing China Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom Cognitive Signal-Image and Control Processing Research Laboratory School of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom

ISBN: (纸本)9781538630655

To address the challenging problem on target recognition from synthetic aperture radar (SAR) images, a novel method is proposed by combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM). First, an improved DCNN is employed to learn the features of SAR images. Then, a SVM is utilized to map the leant features into the output labels. To enhance the feature extraction capability of DCNN, a class of separation information is also added to the cross-entropy cost function as a regularization term. As a result, this explicitly facilitates the intra-class compactness and separability in the process of feature learning. Numerical experiments are performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The results demonstrate that the proposed method can achieve an average accuracy of 99.15% on ten types of targets. © 2017 IEEE.

关键词： Synthetic aperture radar

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H∞interval observer design for uncertain discrete-time linear switched systems with unknown inputs

H∞interval observer design for uncertain discrete-time line...

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2021 International Conference on control, Automation and Diagnosis, ICCAD 2021

作者： Marouani, Ghassen Dinh, Thach Ngoc Sehli, Naima Raissi, Tarek Wang, Zhenhua Messaoud, Hassani University of Monastir National Engineering School of Monastir Automatic Research Laboratory Signal and Image Processing Monastir5019 Tunisia Conservatoire National Des Arts Et Métiers CEDRIC-Lab Paris75141 France University of Tunis National Engineering School of Tunis Conception and Control of Systems Laboratory Le Belvedere 1002 Tunisia Harbin Institute of Technology School of Astronautics Harbin150001 China

ISBN: (纸本)9781665449687

This paper deals with unknown input interval observers for discrete-time linear switched systems. A new structure of interval observer is used to overcome the design difficulty of coordinate transformation often used in such context. The interval observer gains are computed by solving Linear Matrix Inequalities (LMI) derived from multiple quadratic Lyapunov functions (MQLF) under average dwell time switching signals. © 2021 IEEE.

关键词： Linear matrix inequalities

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Change-point analysis of single-trial EEG of auditory and visual oddball tasks

Change-point analysis of single-trial EEG of auditory and vi...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Asian S Dizaji Hamid Soltanian-Zadeh School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Radiology Image Analysis Laboratory Detroit MI United States Control and Intelligent Processing University of Tehran Tehran Iran

ISBN: (纸本)9781467387903

The brain rhythms defined by specific frequency-bands of electroencephalography (EEG) signal are generally thought to represent diverse cognitive processes which wax and wane with sub-second resolutions at different parts of the cortex. Furthermore, single-trial analysis of EEG is believed to show more realistic pictures of ongoing and event-related activities of the brain with millisecond resolution. Here, we present a nonparametric multiple change-point detection and estimation method for analysis of single-trial EEG recorded during auditory and visual oddball tasks. In a simple attention task like oddball, the frontal cortex of the brain is responsible for distinguishing and responding appropriately to target and standard events. With sub-second resolution at the frontal cortex, we show that the α-band activity changes according to “inhibition timing” hypothesis and the β-band activity is in line with “maintaining the status quo” hypothesis.

关键词： Electroencephalography Visualization Electrodes Standards Finite impulse response filters Power harmonic filters Estimation

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Capacity Analysis of Finite Scatterer MIMO Wireless Channels

Capacity Analysis of Finite Scatterer MIMO Wireless Channels

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IEEE International Conference on Communications

作者： Shangbin Wu Cheng-Xiang Wang Bo Ai Yejun He Joint Research Institute for Signal and Image Processing Heriot-Watt University Edinburgh UK State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China College of Information Engineering Shenzhen University Shenzhen Guangdong China

This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas but also the finite angular resolution of antenna arrays. The probability mass function (PMF) of rank is studied and derived based on the Stirling number of the second kind. As a result, the closed-form expression of the capacity of a FS MIMO channel model is obtained. Furthermore, asymptotic analyses such as the large-scale system analysis, infinite scatterer analysis, and the multiplexing gain analysis are investigated.

关键词： MIMO Antenna arrays Channel models Directive antennas Channel capacity Receivers Multiplexing

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Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels

arXiv

引用

arXiv 2023年

作者： Yuan, Yi Wang, Cheng-Xiang Cheng, Xiang Ai, Bo Laurenson, David I. The Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom The Research Institute for Modern Communications School of Electronics Engineering & Computer Science Peking University Beijing100871 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh EdinburghEH9 3JL United Kingdom

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models. © 2023, CC BY.

关键词： Stochastic systems

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