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A Multiscale Gradient Fusion Method for Color image edge detection Using CBM3D Filtering

SENSORS 2025年第7期25卷 2031-2031页

作者： Feng, Zhunruo Shi, Ruomeng Jiang, Yuhan Han, Yiming Ma, Zeyang Ren, Yuheng Xian Polytech Univ Sch Elect & Informat Xian 710048 Peoples R China Xian Jiaotong Liverpool Univ Sch Int Business Sch Suzhou Suzhou 215123 Peoples R China Univ Sains Malaysia Sch Arts Minden 11700 Penang Malaysia Jimei Univ Sch Digital Ind Xiamen 361021 Peoples R China

In this paper, we present a novel color edge detection method that integrates collaborative filtering with multiscale gradient fusion. The Block-Matching and 3D (BM3D) filter is utilized to enhance sparse representations in the transform domain, effectively reducing noise. The multiscale gradient fusion technique compensates for the loss of detail in single-scale edge detection, thereby improving both edge resolution and overall quality. RGB images from the dataset are converted into the XYZ color space through mathematical transformations. The Colored Block-Matching and 3D (CBM3D) filter is applied to the sparse images to reduce noise. Next, the vector gradients of the color image and anisotropic Gaussian directional derivatives for two scale parameters are computed. These are then averaged pixel-by-pixel to generate a refined edge strength map. To enhance the edge features, the image undergoes normalization and non-maximum suppression. This is followed by edge contour extraction using double-thresholding and a novel morphological refinement technique. Experimental results on the edge detection dataset demonstrate that the proposed method offers robust noise resistance and superior edge quality, outperforming traditional methods such as Color Sobel, Color Canny, SE, and Color AGDD, as evidenced by performance metrics including the PR curve, AUC, PSNR, MSE, and FOM.

关键词： image edge detection CBM3D filter anisotropic Gaussian derivative XYZ color model

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Real-Time Aerial image edge detection with MicroWatt Power ISA on FPGA

Real-Time Aerial Image Edge Detection with MicroWatt Power I...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Prashant V Joshi Sudharshan K M Majunath G Asuti Poornashree S J Sunitha K Research School of ECE REVA University Bengaluru India Department of ECE BNMIT Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research investigates the implementation of real-time aerial image edge detection using the Canny edge detection algorithm with the MicroWatt Power Instruction Set Architecture (ISA)-Open Core Processor on Field Programmable Gate Arrays (FPGAs). Canny edge detection is particularly advantageous for aerial image processing due to its ability to produce precise and well-defined edges, making it ideal for applications in surveillance, agriculture, and environmental monitoring where detail preservation is critical. The proposed system utilizes the MicroWatt Power ISA-Open core processor to optimize resource utilization, achieving high throughput and low latency while maintaining energy efficiency. The work uses the Xilinx Vivado EDA tool to calculate the power dissipation of the FPGA implementation, demonstrating significant reductions in power consumption compared to traditional processing methods. The experimental results showcase the effectiveness of the Canny edge detector in producing accurate edge maps while minimizing resource usage, thus enabling real-time processing capabilities. This work contributes to the advancement of low-power embedded systems for aerial imaging, highlighting the potential of FPGAs combined with the MicroWatt Power ISA for efficient and effective image analysis.

关键词： image edge detection Instruction sets Surveillance Imaging Throughput Real-time systems Power dissipation Resource management Environmental monitoring Field programmable gate arrays

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image edge detection based tool condition monitoring with morphological component analysis

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ISA TRANSACTIONS 2017年 69卷 315-322页

作者： Yu, Xiaolong Lin, Xin Dai, Yiquan Zhu, Kunpeng Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Inst Adv Mfg Technol Huihong BldgChangwu Middle Rd 801 Changzhou 213164 Jiangsu Peoples R China Wuhan Univ Technol Sch Logist Engn Heping Rd 1178 Wuhan 430063 Hubei Peoples R China

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Tool condition monitoring image edge detection Morphological component analysis

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image edge detection Based on Swarm Intelligence Using Memristive Networks

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2018年第9期37卷 1774-1787页

作者： Pajouhi, Zoha Roy, Kaushik Intel Corp Santa Clara CA 95054 USA Purdue Univ W Lafayette IN 47907 USA

Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of new computing models. Researchers have shown the suitability of memristive devices for swarm intelligence algorithms to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony optimization. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to 5 x less area.

关键词： AgS memristor ant colony elements with memory gap-type memristor image edge detection image processing memcomputing memory memristor model memristors neural computing NP-complete Silver memristor stochastic processing swarm intelligence

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image edge detection Based on Oscillation

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Journal of Donghua University(English Edition) 2005年第3期22卷 88-91页

作者：范宏王直杰 Glorious Sun School of Business and Management Donghua UniversityShanghai 200051 College of Information Science and Technology Donghua UniversityShanghai 200051

A new method for image edge detection based on a pulse neural network is proposed in this paper. The network is locally connected. The external input of each neuron of the network is gray value of the corresponding pixel. The synchrony of the neuron and its neighbors is detected by detection neurons. The edge of the image can be read off at minima of the total activity of the detection neurons.

关键词： image edge detection pulse neural network synchrony

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image edge detection based on fusion of wavelet transform and mathematical morphology 11

Image edge detection based on fusion of wavelet transform an...

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11th International Conference on Computer Science & Education (ICCSE)

作者： Zhang, Hong-xin Li, Jing-yao Wang, Ming-zhu Li, He Harbin Univ Sci & Technology Inst Machine Intelligence Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781509022182

In order to realize the leading role of machine vision in intelligent cargo handling of the robot, a fusion method of visual image edge detection based on wavelet transform and mathematical morphology is proposed. That wavelet transform is used for image edge detection exists missing detection, smoothness and weak edge problem. So an improved method needs to be studied, in which a wavelet transform and improved mathematical morphology are respectively used to extract the edge of the original image, and then a fusion of two methods is done to obtain a better edge detection after threshold processing. Comparing the proposed algorithm with several classical edge detection algorithms, the experimental result shows that edge detection method proposed in this paper can effectively reduce the influence of noise and to a certain extent enhance the weakening edge, improve the accuracy of the image detection.

关键词： Machine vision image edge detection Wavelet transform Mathematical morphology

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PCNN Model Analysis and Its Automatic Parameters Determination in image Segmentation and edge detection

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Chinese Journal of Electronics 2025年第1期23卷 97-103页

作者： Xiangyu Deng Yide Ma School of Information Science and Engineering Lanzhou University Lanzhou Gansu China School of Electronics and Information Engineering Lanzhou Institute of Technology Lanzhou Gansu China

The Pulse coupled neural network (PCNN) has been widely used in digital image processing, but the automatic parameters determination is still a difficult aspect, which becomes the focus of PCNN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neurons, and reveal the “mathematics firing” phenomenon of PCNN. Based on this, we propose a new method of automatic parameters determination based on both eliminating the “mathematics firing” and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Experimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge detection of the test images.

关键词： image segmentation Analytical models Firing image edge detection Difference equations Neurons Education Mathematical models Time-domain analysis Biological neural networks

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image edge detection Based on Contourlet Transform Combined With the Model of Anisotropic Receptive Fields 5

Image Edge Detection Based on Contourlet Transform Combined ...

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5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Liu Xia Yu Fangfei Sun Ligang Northeast Petr Univ Daqing 163318 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781479942626

The existing algorithms of image edge detection based on space domain can effectively detect the edge of the image in limited direction. In order to solve this problem, according to the direction information and gradient direction information obtained from each directional subband of the Contourlet transform subband, this paper proposes a new algorithm of Based on Contourlet transform combined with anisotropic receptive field model of image edge detection. This algorithm firstly carries Contourlet transform on the original image, and detects the edge on each scale. It compensates and corrects the image edge with high and low frequency respectively. Finally, the edge image is obtained by choosing the coefficient of absolute value maximum. The experimental results show that this algorithm is a new way with continuous edge and accurate positioning, which reduces the false edge.

关键词： image Processing image edge detection Contourlet Transform The Model of Anisotropic Receptive Fields

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image edge detection based on multi-fractal spectrum analysis

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Frontiers of Electrical and Electronic Engineering in China 2006年第2期1卷 146-152页

作者： WANG Shao-yuan WANG Yao-nan College of Electrical and Information Engineering Hunan UniversityChangsha 410082China

In this paper,an image edge detection method based on multi-fractal spectrum analysis is *** coarse grain Holder&&exponent of the image pixels is first ***,its multi-fractal spectrum is estimated by the kernel estimation ***,the image edge detection is done by means of different multi-fractal spectrum *** results show that this method is efficient and has better locality compared with the traditional edge detection methods such as the Sobel method.

关键词： image edge detection Multi-fractal spectrum Holder singular exponent Kernel estimation

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image edge detection using Pseudo-Boolean Polynomials 16

Image Edge Detection using Pseudo-Boolean Polynomials

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16th International Conference on Machine Vision (ICMV)

作者： Chikake, Tendai Mapungwana Goldengorin, Boris Moscow Inst Phys & Technol Dept Discrete Math Phystech Sch Appl Math & Informat Inst Lane 9 Dolgoprudnyi 141700 Moscow Region Russia Pskov State Univ Sci & Educ Math Ctr Sofia Kovalevskaya Northwestern Ctr Math Res Sovetskaya Ulitsa 21 Pskov 180000 Pskovskaya obla Russia

ISBN: (纸本)9781510674639;9781510674622

We introduce a novel approach for image edge detection based on calculating pseudo-Boolean polynomials on image patches whose resulting polynomial degrees determine whether a patch lies over an edge or a blob. In this paper we show that patches covering edge regions within the image result in pseudo-Boolean polynomials of higher degrees compared to patches that cover blob regions. The proposed approach is based on reduction of polynomial degree and equivalence properties of penalty-based pseudo-Boolean polynomials.

关键词： image edge detection pseudo-Boolean polynomials edge and blob regions

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