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检索条件"任意字段=Conference on Nonlinear Image Processing XI"

共 188 条记录，以下是31-40 订阅

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Communication Base Station Traffic Prediction Based on nonlinear Regression-LSTM Decomposition Algorithm

Communication Base Station Traffic Prediction Based on Nonli...

引用

Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Yanhe Tao >School of Marine Science and Technology Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Communication base station traffic prediction can help network service providers predict the peak hours of network traffic, so as to carry out network expansion and load balancing in advance, and keep the users getting a smooth network experience even during the peak hours of the network. In order to accurately predict traffic usage in different time periods, we first constructed a nonlinear regression model, then solved the parameters in the algorithm, and finally got the image of our predicted nonlinear function and compared it with the real value, and found that there are some small swings. Finally, we use the LSTM recurrent neural network to predict the swing term. The final flow prediction is obtained which is very close to the real situation. The model performance was verified with MAE, MAPE and RMSE metrics respectively, and the results showed that the values of the three metrics were 0.1873, 0.0978 and 0.2260 respectively.

关键词： Measurement Analytical models Base stations Recurrent neural networks Time series analysis Signal processing algorithms Telecommunication traffic Predictive models Prediction algorithms Long short term memory

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A Parametric Non-Negative Coupled Canonical Polyadic Decomposition Algorithm for Hyperspectral Super-Resolution

A Parametric Non-Negative Coupled Canonical Polyadic Decompo...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： xi-Yuan Liu xiao-Feng Gong Lei Wang Wei Feng Qiu-Hua Lin School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications.

关键词： Jacobian matrices Tensors Superresolution Noise Signal processing algorithms Matrix decomposition Matrix converters Speech processing Optimization Hyperspectral imaging

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A Gray-level Morphological Operation Method Based on the Umbra Notion

A Gray-level Morphological Operation Method Based on the Umb...

引用

IEEE International Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Yimeng Zhang Junping Wang Yuyu Li School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (数字)9798331505639

ISBN: (纸本)9798331505646

Mathematical morphology as a nonlinear image processing theory has been used in various aspects of image processing. However, with the emergence of morphological networks and the increasing resolution of images, the demand for the speed of morphological image processing is increasing. In this paper, we propose an implementation method of binary operations for gray-level morphology based on the umbra notion. In this method, firstly, the gray-level image to be calculated and the structural element image are converted into the corresponding binary umbra image according to the umbra notion. Then, dilation, erosion, opening and closing operations are performed on the umbra of the structural element image and the image to be processed to obtain the corresponding umbra result image. Finally, the umbra result image is converted into its top surface based on the linkage of the binary image with the pixel value of the gray-level image to obtain the corresponding gray-level image dilation, erosion, opening and closing operational results. The experimental results on one-dimensional and two-dimensional images demonstrate the effectiveness of the method, which can lay a foundation for the fast calculation of gray-level morphological images.

关键词： Couplings image resolution Fast Fourier transforms Surface morphology Morphology Hardware Security Surface treatment Morphological operations Erosion

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Superquadric Modeling Method of 3D Object Based on Invasive Weeds Optimization and Levenberg-Marquardt Algorithms

Superquadric Modeling Method of 3D Object Based on Invasive ...

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Lin Wang Donghan Ma Xuan Duan Shengmei Chen Boyue Duan Cheng Liu Pu Zhang School of Information Science and Technology Northwest University Xi’an China School of Information Engineering Xi’an Mingde Institute of Technology Xi’an China

Because of the problem of insufficient parameter fitting accuracy of superquadric modeling method based on Levenberg-Marquardt (LM) algorithm, a superquadric modeling method of 3D object based on invasive weed optimization (IWO) algorithm and LM algorithm is proposed. First, according to the distance relationship between each point in point cloud and the superquadric, a nonlinear optimization model for superquadric parameter estimation of 3D object is established. Then, to obtain the initial values of superquadric fitting parameters, the IWO algorithm is adopted to optimize the optimization model. After that, LM algorithm is also adopted to optimize the optimization model, and estimation values of superquadric fitting parameters of 3D object can be obtained. Thus, the superquadric model of 3D object of the given point cloud can be established. The experimental results show that the proposed method can yield highly accurate superquadric fitting parameters, and can effectively suppress the influence of measurement noise on modeling accuracy as well.

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A Novel Adaptable Sonar image Pseudo-color Enhancement Method Using CIELab Space

A Novel Adaptable Sonar Image Pseudo-color Enhancement Metho...

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2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Li, Qiuju Xu, Haiping Lan, Zhixiang Kunming Precision Machinery Research Institute Kunming China Northwest University Xi'an China

ISBN: (纸本)9781665429184

A novel adaptable sonar image pseudo-color enhancement method using CIELab space is introduced in this paper. This method gets the pseudo-color image through establishing a nonlinear mapping relationship between the gray sonar image and the pseudo-color image. Firstly, this method gets the color information of the pseudo-color image and the intensity information of gray sonar image through the transformation form RGB space to CIELab space. Secondly, put the color information and the intensity information together and inversely transform them from CIELab space to RGB space. Then the pseudo-color image is obtained. As well, three objective evaluation methods will be used to evaluate the novel method. The experimental results show that the pseudo-color image obtained using the novel method has more rich colors and clear details, and it is a good sonar image pseudo-color enhancement method. © 2021 IEEE.

关键词： Color

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Infrared image Target Recognition Tracking Complexity Metrics

Infrared Image Target Recognition Tracking Complexity Metric...

引用

IEEE International conference on Control and Robotics Engineering (ICCRE)

作者： Sijia Li Yao Yang Qiyan Liu Dangshuai Song Unmanned Systems Research Institute Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

On the basis of the research on the working principle of infrared interference, the feature extraction processing of the target region and interference region from the infrared sequence image is carried out to obtain the mixed feature vector of each region, and combined with the knowledge of the image complexity, the similarity metrics and masking metrics are constructed on the basis of the specific factors affecting the target recognition and its tracking. Then the integrated complexity of infrared image sequence is realized by choosing suitable weighted average function and nonlinear transformation function. Finally, the validity of the proposed index is verified through the analysis of experimental results, and the integrated complexity of infrared image sequence can correctly represent the difficulty of the target recognition and tracking process of the infrared guide head, which can be used to evaluate the effect of infrared decoy jamming and further guide the selection of jamming projection strategy.

关键词： Performance evaluation Target tracking Target recognition Interference Feature extraction Vectors Complexity theory

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An Imaging Method for Ship Target Based on Rotation Parameters Estimation and Extended nonlinear Chirp Scaling

An Imaging Method for Ship Target Based on Rotation Paramete...

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IEEE International conference on Information Communication and Signal processing (ICICSP)

作者： Tao Chen Jinsong Lin Qing Yang Zhongyu Li Junjie Wu Jianyu Yang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In this paper, the problem of imaging targets with uniform rotation is investigated, and a solution method based on genetic algorithm and extended nonlinear chirp scaling is proposed. Firstly, the keystone transform and high-order range cell migration are performed to accomplish motion compensation. Then, the differential evolution (DE) algorithm, a mutant of the genetic algorithm, is utilized to search the optimal rotation parameters. In DE algorithm, the image entropy is adopted as the object function. Simulation results validate that the proposed method can estimate rotation parameters accurately and achieve desirable peak side-lobe ratio and integration side-lobe ratio.

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An Unsupervised Domain Adversarial Wavelet Learning Method for registration of Optical and SAR Heterogeneous Remote Sensing images

An Unsupervised Domain Adversarial Wavelet Learning Method f...

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Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Hongze Ye Shenfu Zhang Liangzhi Li xiangchao Meng School of Information Science and Engineering Ningbo University Ningbo China College of Geological Engineering and Geomatics Chang’an University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets.

关键词： Optical filters Wavelet transforms Wavelet domain Optical distortion Radiometry Optical imaging Optical sensors nonlinear optics Synthetic aperture radar Remote sensing

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Study on Ellipse Detection Method Based on KMPE Loss Function

Study on Ellipse Detection Method Based on KMPE Loss Functio...

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image processing and Media Computing (ICIPMC), International conference on

作者： Mengwei Wang Wanxu Zhang Lu Ren Na Meng Lin Wang School of Information Science and Technology Northwest University Xi’an China

For point set containing noise and outliers, the existing ellipse detection methods have low detection accuracy. Aiming at this problem, an ellipse detection method based on kernel mean p-power error (KMPE) loss function is proposed. Firstly, the point set to be detected is pre-processed according to the 3$\sigma$ criterion, and the noise points and outliers with large random errors are filtered out. Secondly, KMPE loss function is introduced, and according to distance constraint of each point on the ellipse to its focuses, a robust nonlinear optimization model of ellipse detection based on KMPE is established. Thirdly, success-history based parameter adaptation for differential evolution (SHADE) algorithm is adopted to solve the optimization model, and the initial estimated parameters of the detected ellipse is obtained. And the data points are divided into inliers and outliers according to the initial estimated parameters. Finally, the SHADE algorithm is adopted again to optimize the optimization model with inliers, and the accuracy estimated parameters of the detected ellipse can be obtained. Experimental results show that the proposed method has high accuracy of ellipse detection, and can effectively suppress the influence of noise and outliers on ellipse detection accuracy.

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A Gradient-based Method with Denoising for Target Shadow Profile Retrieval in Forward Scatter Radar

A Gradient-based Method with Denoising for Target Shadow Pro...

引用

Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： xi Shen Defeng David Huang Department of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The existing nonlinear least-squares method for target shadow profile retrieval in forward scatter radar only considers noiseless samples of the forward scatter shadow ratio. Inspired by optical diffraction tomography, in this paper we propose to use a gradient-based method with denoising to tackle scenarios involving noisy samples. Simulation results demonstrate its superiority over the conventional least-squares method. More specifically, an essential prerequisite for the least-squares method, i.e., the number of samples being greater than or equal to the number of pixels, is no longer required by the proposed method. Moreover, the proposed method retrieves the target image with a higher peak signal-to-noise ratio than that using the least-squares method.

关键词： PSNR Optical diffraction Simulation Noise reduction Noise Radar Radar scattering Tomography Radar imaging Noise measurement

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