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Field-Free Memristive Spin-Orbit Torque Switching in A1 CoPt Single Layer for image edge detection

ADVANCED ELECTRONIC MATERIALS 2024年第8期10卷

作者： Yang, Liu Li, Wendi Zuo, Chao Tao, Ying Jin, Fang Li, Huihui Tang, Rujun Dong, Kaifeng China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430064 Peoples R China Beijing Superstring Acad Memory Technol Beijing 100176 Peoples R China Soochow Univ Sch Phys Sci & Technol Jiangsu Key Lab Thin Films Suzhou 215006 Peoples R China

While spin-orbit torque (SOT) devices are extensively investigated due to their potential for use in neural network computation, it remains challenging to explore the hardware for neural networks. In this paper, the field-free memristive SOT switching of the CoPt single layer is used to propose a neuromorphic hardware circuit for detecting edges in images. Owing to its threefold symmetry of inversion, the polarity of SOT switching can be reversed by rotating the current by 60 degrees. Moreover, the process of current-induced SOT switching exhibits stable multi-state magnetic switching behavior, and can be controllably tuned by using the pulse of the current. As the slope of the applied ramp pulse current increased, the wave of the anomalous Hall resistance changed from a curve with normally memristive property to trigonometric, and finally to cosine. The design of the hardware circuit for a single SOT device is subsequently formulated to detect the edges in images. The results of experiments verified the capability of this device to detect the edge lines in images with high accuracy, which confirms its potential for use in the hardware of neuromorphic computing platforms. The work here provides guidance for the application of SOT-based devices to neuromorphic hardware. The CoPt single layer SOT devices can realize magnetic field-free switching, whose polarity can be reversed when the current is rotated by 60 degrees, and anomalous Hall resistance can be controlled by using different current pulses. In addition, an analog circuit is proposed to detect the edges of images based on the binary-state magnetic switching of the CoPt single layer. image

关键词： field-free switching hardware neuromorphic computing image edge detection multistate memory spin-orbit torques

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QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection

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SIGNAL PROCESSING 2023年第1期212卷

作者： Kaur, Kanwarpreet Jindal, Neeru Singh, Kulbir Chandigarh Univ Mohali 140413 Punjab India Thapar Inst Engn & Technol Bhadson Rd Patiala 147004 Punjab India

edge detection is the prominent method to determine the discontinuities present in an image. There ex-ists an issue of loss of information and correlation during the extraction of edges in color images. To resolve it, a novel quaternion domain based Riesz fractional order directional derivative approach is pre-sented in this paper to extract more edge details in color images. The Riesz fractional masks obtained by utilizing Aitken interpolation are applied on color images in the quaternion domain for yielding the edge map as there is no need for recalculation of basis polynomials in case of the addition of any data point. The performance of the proposed quaternion based technique is analyzed on the benchmark BSDS300 and BSDS500 datasets in the terms of Figure of Merit and F-Score. The results obtained exhibit the efficiency of proposed technique over the classical and state-of-the-art edge detection approaches. The robustness of this approach is further established by taking into account uncontrolled conditions such as noise, JPEG compression, and varying illumination as it provided more features than the classical integer-order de-tectors. Moreover, the superiority of the proposed quaternion based technique is validated for medical images as well as its application in image enhancement. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Color images Fractional calculus image edge detection Riesz fractional order derivative Quaternion

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A Filter image edge detection Algorithm Based on PCNN

World Scientific Research Journal

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World Scientific Research Journal 2024年第2期10卷 77-90页

作者： Xiangyu Deng Haiyue Yu Xikai Huang

As a basic task in image processing, edge detection is widely used in advanced vision tasks. Traditional edge detection algorithms, such as Canny and Sobel, start with the gradient of the image, select the place where the pixel gradient changes obviously, and complete the edge and contour extraction. It has the characteristics of fast speed and immediacies, but the extracted edge is easily affected by noise. To solve this problem, this paper proposes a filter image edge detection algorithm based on PCNN. Starting from the spectrum component information of the image, noise interference is filtered first, and then the noise is further filtered through PCNN model iteration to obtain the optimal binary image of the image, and finally the edge information is extracted through Gauss high‐pass filter. Experimental results show that the proposed algorithm is superior to the traditional edge detection operators under the evaluation of PSNR, MSE, FOM and ELE, and can meet the requirements of practical tasks while maintaining the edge of image details.

关键词： PCNN Filter Traditional edge detection operator image edge detection

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Grayscale image edge detection Based on Pulse-Coupled Neural Network and Particle Swarm Optimization

Grayscale Image Edge Detection Based on Pulse-Coupled Neural...

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20th Chinese Control and Decision Conference

作者： Wang, Jiesheng Cong, Fengwu Liaoning Univ Sci & Technol Sch Elect & Informat Engn Anshan 114051 Peoples R China Liaoning Univ Sci & Technol Sch Mat Sci & Engn Anshan 114051 Peoples R China

ISBN: (纸本)9781424417339

Pulse coupled neural network (PCNN) was originally presented to explain the synchronous burst of the neurons in the cat visual cortex by Eckhorn. Because the parameters greatly affect the performance of PCNN, finding the optimal parameters becomes an onerous task. Particle swarm optimization (PSO) is a global stochastic evolutionary algorithm It tries to find optimal regions of complex searching space through the interaction of particles in the population. A self-tuning optimized method for PCNN parameters based on PSO algorithm and it was used to detect edges in a gray image automatically and successfully. The effective of the proposed method is verified by simulation results, that is to say, the quality of the image edge detection is much better and parameters are set automatically.

关键词： Pulse-Coupled Neural Network (PCNN) Particle Swarm Optimization (PSO) image edge detection

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Training a neural network for moment based image edge detection

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Operation behaviors of ideal internal thermally coupled distillation columns 2000年第4期1卷 398-398页

作者： WANG Hong-yu LI Hong-dong YE Xiu-qing GU Wei-kang Department of Information and Electronics Zhejiang University Hangzhou China

edge detection is a crucial step to computer vision. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness, but require excessive post-processing. This paper introduces a neural network edge detector that takes advantage of moments features. It functions as a neural pattern classifier that directly estimates the posterior probability from the training data set. Two subsystems can be distinguished and different kinds of learning rules are used. For the end-user, it works as a black box that directly transforms raw images into the edge maps so no complicated postprocessing is required. Tests on both simulated and real images showed the proposed neural network edge detector is superior to traditional operators.

关键词： image edge detection

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Optimization of Processor Architecture for image edge detection Filter

Optimization of Processor Architecture for Image Edge Detect...

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International Conference on Computer Modeling and Simulation, UKSIM

作者： Zahraa Elhassan M. Osman Fawnizu Azmadi Hussin Noohul Basheer Zain Ali Electrical and Electronic Engineering Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

ISBN: (纸本)9781424466146;9780769540160

In this paper, a dedicated edge detection processor architecture based on field programmable gate arrays is presented. The architecture is an optimization of the Sobel edge detection filter, specifically focusing on the reduction of the computation time. The proposed architecture reduces the number of calculations required for the edge detection process by enhancing the data reuse, i.e. minimizing the frequency of memory access. Direct hardware implementation as proposed by previous works require most image pixels to be read from memory up to six times and transferred into the Sobel edge detection processor. In our work, we try to reduce the number of pixels read therefore affecting tremendous potential speed suitable for the embedded video processing applications.

关键词： image edge detection Computer architecture Filters Brightness Field programmable gate arrays image processing Real time systems Pixel image segmentation Motion detection

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A novel neural network approach for image edge detection

A novel neural network approach for image edge detection

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International Conference on Electrical Engineering and Software Applications (ICEESA)

作者： Sabeur Abid Farhat Fnaiech Ezzeddine Ben Braiek Research team: Signal Image and Intelligent Control of Industrial Systems: SICISI) Ecole Supérieure des Sciences et Techniques de Tunis Tunis Tunisia Centre de Recherche en Productique CEREP) Ecole Supérieure des Sciences et Techniques de Tunis Tunis Tunisia

ISBN: (纸本)9781467363020

In this work a new method for image edge detection based on multilayer perceptron (MLP) is proposed. The method is based on updating a MLP to learn a set of contours drawn on a 3×3 grid and then take advantage of the network generalization capacity to detect different edge details even for very noisy images. The method is applied first to Gray scale images and can be easily extended to color ones. Simulations on synthetic and real image show much promised results in term of precision and localization. Moreover the method works well even for very low contrast images for which other edge operators fail.

关键词： image edge detection Detectors Databases Shape Noise measurement Biological neural networks

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FPGA-based Floating Point Fractional Order image edge detection

FPGA-based Floating Point Fractional Order Image Edge Detect...

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International Computer Engineering Conference (ICENCO)

作者： Omar H. Moustafa Samar M. Ismail Faculty of Information Engineering and Technology (IET) German University in Cairo (GUC) Egypt Faculty of Engineering and Technology Egyptian Chinese University (ECU) Egypt

ISBN: (数字)9781728151465

ISBN: (纸本)9781728151472

In this paper, the hardware implementation of an image edge detector based on fractional order filters is presented. Fractional order filters excel over their integer order counterparts in their noise robustness and in detecting the high frequency details of images, which is very useful for biomedical image processing. The fractional order parameter also offers an extra degree of freedom in designing the fractional order filter to achieve higher efficiency than integer ones. The hardware design proposed exploits the single precision floating point arithmetic, IEEE 754 representation, to have precision in implementation. The filters were applied to standard images as well as an MRI scan as a biomedical image processing application. The hardware results obtained are the same as the Matlab simulations. The design is implemented on VIRTEX 5 XC5VSX50T-2FF1136 FPGA kit achieving a clock frequency of 170.1 MHz.

关键词： image edge detection Convolution Hardware Standards Field programmable gate arrays Biomedical imaging Matlab

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Single Parameter Logarithmic image Processing for edge detection

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第11期E96D卷 2437-2449页

作者： Ren, Fuji Li, Bo Chen, Qimei Univ Tokushima Fac Engn Tokushima 7708501 Japan Nanjing Univ Sch Elect Sci & Engn Nanjing 210008 Jiangsu Peoples R China

Considering the non-linear properties of the human visual system, many non-linear operators and models have been developed, particularly the logarithmic image processing (LIP) model proposed by Jourlin and Pinoli, which has been proved to be physically justified in several laws of the human visual system and has been successfully applied in image processing areas. Recently, several modifications based on this logarithmic mathematical framework have been presented, such as parameterized logarithmic image processing (FLIP), pseudo-logarithmic image processing, homomorphic logarithmic image processing. In this paper, a new single parameter logarithmic model for image processing with an adaptive parameter-based Sobel edge detection algorithm is presented. On the basis of analyzing the distributive law, the subtractive law, and the isomorphic property of the PLIP model, the five parameters in PLIP are replaced by a single parameter to ensure the completeness of the model and physical constancy with the nature of an image, and then an adaptive parameter-based Sobel edge detection algorithm is proposed. By using an image noise estimation method to evaluate the noise level of image, the adaptive parameter in the single parameter LIP model is calculated based on the noise level and grayscale value of a corresponding image area, followed by the single parameter LIP-based Sobel operation to overcome the noise-sensitive problem of classical LIP-based Sobel edge detection methods, especially in the dark area of an image, while retaining edge sensitivity. Compared with the classical LIP and FLIP model, the given single parameter LIP achieves satisfactory results in noise suppression and edge accuracy.

关键词： image edge detection single parameter logarithmic image processing image noise measurement

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Research and Application of Metallographical image edge detection Based on Mathematical Morphology

Research and Application of Metallographical Image Edge Dete...

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International Symposium on Intelligent Information Technology Application, IITA

作者： Mingxing Jiang Guohua Chen College of Science South China Agricultural University Guangzhou China School of Mechanical and Automotive Engineering South China University of Technology China

Aimed at the characteristic of metallographical image, a specific image, and based on the multi-structural element in the mathematical morphology, a new method of edge detection is proposed in this paper to deal properly with the double- boundary and the inaccuracy of the boundary localization in the image in the edge detection carried by the traditional edge detection method, such as Roberts, Sobel, Prewitt, Log, Canny and so on. The design of the multi-structural element and the construction of the suitable morphology operator are emphasized in this paper. The experimental results indicated that this method is very effective, and is better than the traditional edge detection method.

关键词： image edge detection Morphology Inorganic materials Materials testing image processing Materials science and technology Performance analysis image segmentation Intelligent vehicles Information technology

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