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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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Inverse synthetic aperture radar imaging using complex-value deep neural network

Inverse synthetic aperture radar imaging using complex-value...

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IET International Radar conference 2018, IRC 2018

作者： Hu, ChangYu Wang, Ling Li, Ze Sun, Lingling Loffeld, Otmar Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Center for Sensor Systems University of Siegen Siegen Germany

As compared with traditional ISAR imaging methods, the compressive sensing (CS)-based imaging methods can obtain high-quality images using much less under-sampled data. However, the availability or appropriateness of the sparse representation of the target scene and the relatively low computational efficiency of image reconstruction algorithms limit the performance and application of the CS-based ISAR imaging methods. In recent years, the deep learning technology has been applied in many fields and achieved outstanding performance in image classification, image reconstruction etc. DL implements the tasks using the deep neural network (DNN), which composes multiple hidden layers and non-linear activation layer. In this study, a novel ISAR imaging method that uses a complex-value deep neural network (CV-DNN) to perform the image formation using under-sampled data is proposed. The CV-DNN architecture can extract and exploit the sparse feature of the target image extremely well by multilayer non-linear processing. The experimental results show that the proposed CV-DNN-based ISAR imaging method can provide better shape reconstruction of target with less data than state-of-the-art CS reconstruction algorithms and improve the imaging efficiency obviously. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Deep neural networks

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Skinflow: a soft robotic skin based on fluidic transmission

Skinflow: a soft robotic skin based on fluidic transmission

引用

2nd IEEE International conference on Soft Robotics (RoboSoft)

作者： Soter, Gabor Garrad, Martin Conn, Andrew T. Hauser, Helmut Rossiter, Jonathan Univ Bristol Dept Engn Math Bristol BS8 1TH Avon England Bristol Robot Lab Bristol BS16 1QY Avon England Univ Bristol Dept Mech Engn Bristol BS8 1TH Avon England

ISBN: (纸本)9781538692608

In this paper we present Skinflow, a novel soft robotic sensor based on liquid transmission. The sensor combines liquid filled soft silicone chambers and optical sensors to measure pressure, bending and vibration. When mechanically stimulated, the volume of the chambers changes and this change is transmitted to a display cell by an incompressible, coloured liquid. The displacement of the liquid in the channels is captured by a CCD camera and is quantified by image processing algorithms. We present three implementations of this concept. The first device is a soft button array with four pressure sensitive buttons. The second implementation is a three-dimensional soft touchpad, that consists of two sensor layers oriented at 90 degrees to each other. Both layers have eight macrochannels that are filled with coloured liquid. The sensor is able to measure the position and the intensity of the touch. Finally, the third device shows how the processing unit for the proposed liquid filled sensors can be integrated using a smart vision camera and a microcontroller. Three soft bend sensors are connected to the processing unit and we control the brightness of three light emitting diodes (LEDs) in real-time by bending them. The presented sensors are safe, low cost and scalable. They can be used in soft robots, smart homes and wearables as well as for medical diagnosis, especially where electronic devices cannot be used due to electromagnetic interference (e.g. magnetic resonance imaging).

关键词： Robot sensing systems Liquids Cameras Magnetic liquids Optical sensors Force Soft robotics

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Gradient management and algebraic reconstruction for single image super resolution 17

Gradient management and algebraic reconstruction for single ...

引用

17th image processing: algorithms and systems conference, IPAS 2019

作者： Delfin, Leandro Morera Elias, Raul Pinto de Jesús Ochoa Domínguez, Humberto Center of Investigation and Technological Development CENIDET Morelos Mexico Universidad Autónoma de Ciudad Juárez UACJ Chihuahua Mexico

In this paper, a single image multi-scale super-resolution technique is proposed. The concept under study is the learning procedure between steps of amplification in order to predict the next high scale of resolution. The method integrates two different approaches for the prediction of a high resolution multi-scale scheme, a pure interpolation and a gradient regularization. In the first step a pure interpolation is carried out. It is used a prediction scheme with algebraic reconstruction through different scales to produce the high resolution output. In the last step, the residual blur is reduced by a gradient auto-regularization method. The gradients are adapted by using a weight in a neighbour. Precision of method can be controlled by the parameters of an algebraic reconstruction technique (ART). The proposed model avoids the fast decrease of the output resolution as the amplification factor increases. The proposed system was tested with a dictionary. Results show that the output image quality is improved despite of the increment of the scale factor. © 2019, Society for Imaging Science and Technology.

关键词： Interpolation

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Formal Verification of Neural Network Controlled Autonomous systems 22

Formal Verification of Neural Network Controlled Autonomous ...

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22nd ACM International conference on Hybrid systems: Computation and Control (HSCC)

作者： Sun, Xiaowu Khedr, Haitham Shoukry, Yasser Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

ISBN: (纸本)9781450362825

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial states such that a robot trajectory starting from these initial states is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of safe initial states. To mathematically model the imaging function, that maps the robot position to the LiDAR image, we introduce the notion of imaging-adapted partitions of the workspace in which the imaging function is guaranteed to be affine. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) non-linearity, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space of feasible ReLU assignments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with the increasing complexity of the neural network controller.

关键词： Formal Verification Machine Learning Satisfiability Solvers

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Object Tracking Platform for Color Object Detection using Genetic Algorithm Optimization

Object Tracking Platform for Color Object Detection using Ge...

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International conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Yacine Messai Kheireddine Chara Fawzi Srairi Fouzi Douak University of Abbes Laghrour Khenchela Algeria LAAAS University of Batna Algeria

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

The aim of this work is to resolve typical tracking's challenge, which is object detection in the scene. In this context, a new robotic system to detect objects in unknown indoor and outdoor environment is realized. The developed robotic system is equipped with ultrasonic sensor and camera, gives visually information about the environment around detected object. Therefore, the realized system works in real time by analyzing camera data making it possible to detect color, size and position of the object. The aim of the proposed strategy is to find a compromise between robustness and processing speed of color detection based on rapid threshold, which produces a much smaller number of edge pixels compared to standard approaches based on a simple threshold. This reduction significantly reduces the number of votes required for robust real-time detection of object parameters. The approach consists of two main phases. In the first calibration phase, which takes place offline, a heuristic method of color classification using genetic algorithms is learned from a copy of an image of our colored environment. Then, in the real-time monitoring phase, the color classification is applied to the input images, the object is detected and its position is returned. To boost the detection accuracy the fixed and adaptive threshold methods are tested and compared with the genetic algorithm method, where the last method presents a good results. Accordingly, an exact determination of position, orientation of mobile platform, and accurate determination of color object in the environment is succeed.

关键词： image color analysis Object detection Robot sensing systems Genetic algorithms Color Biological cells

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Enhanced Feature Selection for Facial Expression Recognition systems with Genetic algorithms 20th

Enhanced Feature Selection for Facial Expression Recognition...

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20th International conference on Engineering Applications of Neural Networks (EANN) /1st International Workshop on Pervasive Intelligence (PEINT)

作者： Chengeta, Kennedy Univ KwaZulu Natal Dept Math Stat & Comp Sci Westville Campus Durban South Africa

ISBN: (纸本)9783030202576;9783030202569

Humans use voice and facial expressions to infer their state of emotions. Key expressions include being happy, angry, sad and neutral. The expressions accounts for a third of non-verbal communication. The study presents an efficient method to identify facial expressions in images based on artificial neural networks enhanced by genetic algorithms. We use viola Jones for facial detections and PCA, a statistical method to reduce the dimensionality and extract the features with a local variant of local binary patterns called CS-LBP for static images which is a local algorithm that reduces feature set by comparing symmetrical pixels halving the feature set. The features are then optimally selected using genetic algorithm before classification using artificial neural networks. It is also crucial to note that with these emotions being natural reactions, recognition of feature selection and edge detection from the images can increase accuracy and reduce the error rate. This can be achieved by removing unimportant information from the facial images. The genetic algorithm (GA) chooses a subset of image features based on a reduced-dimensional dataset. The study proposes local binary pattern variant central symmetric local directional pattern (CS-LDP), central symmetric LBP (CS-LBP) and artificial neural networks aided by genetic algorithms for feature selection. The study used the Japanese Female Facial Expression, JAFFE database. The approach outperformed other traditional approaches and proved that with added feature selection and optimization the processing time is reduced and accuracy improved.

关键词： Local Directional and Binary patterns Genetic algorithms

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Network Intrusion Monitoring System Wavelet Analysis Traffic

Network Intrusion Monitoring System Wavelet Analysis Traffic

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International Scientific-Practical conference Problems of Infocommunications Science and Technology

作者： Bogdan Petrik Valeriy Dubrovin Hanna Nelasa Yulia Tverdokhlib National University "Zaporizhzhia Polytechnic" Zaporozhye Ukraine FreshCode Zaporozhye Ukraine

The modern development of the field of cybersecurity shows the need for the development of new and modification of existing algorithms for traffic analysis in network systems. A detailed analysis of the simulated traffic using large data processing technologies is presented. The advantage of this technology is that it allows you to show the features of the local structure of a complex signal and to reveal its various properties that are invisible in real time. In the field of wavelet transform, additional information is extracted using the representation in the time-frequency image of a signal that is not available in its original form. The expediency of creating a hybrid network traffic monitoring system based on wavelet analysis has been substantiated. The use of wavelet bases for solving this problem has a leading position. The choice of the optimal wavelet basis will increase the probability of detecting attacks. The combination of several wavelet bases can give a better result in detecting intrusions into computer networks compared to monitoring based on a single wavelet base. When considering different methods of wavelet analysis to solve this problem, not identical examples of simulating attacked network traffic are used. Simulated traffic consisting of a real trace of an attack can show the full effectiveness of this method. Integrated and hybrid experimental research systems will not only save time difference for developing a detection system in Brandmauer itself, but also gain the effectiveness of multiple detection methods.

关键词： Wavelet transforms Time-frequency analysis Intrusion detection Telecommunication traffic Wavelet analysis Feature extraction Data processing

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Least-squares Optimal Relative Planar Motion for Vehicle-mounted Cameras

Least-squares Optimal Relative Planar Motion for Vehicle-mou...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Levente Hajder Daniel Barath Department of Algorithms and their Applications Eotvos Lorand University Budapest Hungary Visual Recognition Group Czech Technical University Prague Czech Republic

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

A new closed-form solver is proposed minimizing the algebraic error optimally, in the least squares sense, to estimate the relative planar motion of two calibrated cameras. The main objective is to solve the over-determined case, i.e., when a larger-than-minimal sample of point correspondences is given - thus, estimating the motion from at least three correspondences. The algorithm requires the camera movement to be constrained to a plane, e.g. mounted to a vehicle, and the image plane to be orthogonal to the ground. 1 The solver obtains the motion parameters as the roots of a 6th degree polynomial. It is validated both in synthetic experiments and on publicly available real-world datasets that using the proposed solver leads to results superior to the state-of-the-art in terms of geometric accuracy with no noticeable deterioration in the processing time.

关键词： Cameras Transmission line matrix methods Robot vision systems Estimation Automobiles Geometry

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Toward Classification of Arabic Manuscripts Words Based on the Deep Convolutional Neural Networks

Toward Classification of Arabic Manuscripts Words Based on t...

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Intelligent systems and Computer vision (ISCV)

作者： Marouan Elmansouri Noureddine EL Makhfi Badraddine Aghoutane IA Laboratory Computer science dept. My Ismail University Morocco PMIC Laboratory Computer science dept. Abdelmalek Essaadi University Morocco

ISBN: (数字)9781728180410

ISBN: (纸本)9781728180427

Deep learning is an area that has seen many developments in recent years. One of these algorithms that have provided good results is Deep Convolutional Neural Networks (DCNN). It is proven to be effective in various fields such as natural language processing, pattern recognition, computer vision, object detection in images, etc. Despite the development of these technologies, Arabic manuscripts in digital libraries still use traditional indexing methods based on metadata, annotation or transcription. In this article, we propose two methods of word classification based on deep learning, the first one uses a simple Neural Network (DNN) and the last one uses a Convolutional Neural Network (DCNN). The idea is to segment words of Arabic manuscripts images and predict the class of each word. The experimental results show the efficient of this classification system based on the DCNN. By comparing the results obtained, we can observe that the DCNN method provides excellent results than those obtained with the DNN method.

关键词： Convolutional neural networks image segmentation Feature extraction Biological neural networks image recognition Databases Character recognition

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Adaptive Weighted Robust Principal Component Analysis1

Adaptive Weighted Robust Principal Component Analysis1

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Zhengqin Xu Yang Lu Jiaxing Wu Rui He Shiqian Wu Shoulie Xie School of Machinery and Automation Institute of Robustics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Institute of Robustics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Institute for Infocomm Research A*STAR Singapore

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Robust principal component analysis (RPCA) via the nuclear norm minimization (NNM) is a powerful tool for image processing problems. However, most of NNM methods only consider the number of non-zero singular values of the observation matrix, and ignore the different proportions of data information in different singular values, which are related to the exact rank of clean data and should be treated differently. In this paper, we propose an adaptive weighted RPCA to simultaneously preserve low-rank structure and restore the corrupted parts. In our method, the sum of weighted singular values is included in the objective function of minimization. We first estimate the rank of the clean data contained in the observation data by Gerschgorin disks method. Then the weights are adaptively updated by considering some singular values based on the estimated rank, thus both the number and size of the singular values are considered to recover the low-rank matrix with correct information. Experimental results show that the proposed adaptive weighted RPCA algorithm can achieve better performance under various conditions compared to the existing algorithms.

关键词： Industrial electronics Lighting Tools Minimization Linear programming image restoration Principal component analysis

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