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Informatica (Slovenia) 2025年第7期49卷 89-100页

作者： An, Yang Yuan, Qianqian Zhang, Han School of Science Jiaozuo Normal College Jiaozuo454002 China

Edge detection plays a crucial role in image processing and computer vision, and is widely used in tasks such as object recognition and image segmentation. Traditional edge detection algorithms perform well in many applications, but there are still some shortcomings in terms of real-time performance and processing efficiency. To address this issue, a highly efficient image edge detection model combining sobel algorithm and field programmable gate array technology was proposed. YCbCr color space conversion was performed on the image, then sobel operator was utilized to calculate the image gradient, and adaptive thresholding method was applied to determine the edges. Finally, the model was implemented and optimized on a field programmable gate array. The experimental results showed that when the dataset size was 1000, the information retention rate of the proposed image preprocessing model was 0.89, and the structural information loss was 0.05. When the data volume was 100, the accuracy of the proposed image edge detection model was 0.90, and the root mean square error value was 0.16. The research results indicate that the proposed image edge detection model based on field programmable gate arrays has significant advantages in edge detection performance and processing efficiency. The model has high accuracy and speed in image edge recognition, which can provide certain guidance for research in the field of image edge detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： edge detection FPGA sobel algorithm YcbCr

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Improved sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy

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Journal of Central South University 2016年第11期23卷 2867-2875页

作者：石甜孔建益王兴东刘钊郑国 School of Machinery and Automation Wuhan University of Science and Technology

A more effective and accurate improved sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the sobel algorithm is improved.

关键词： sobel algorithm surface defect heavy rail experimental platform identification

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Design of English video course platform based on machine learning and sobel algorithm

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SOFT COMPUTING 2023年 1-11页

作者： Zhao, Ling Chen, Lijiao Zhang, Junchi Liu, Ying Wuhan Univ Coll Foreign Languages & Literature Wuhan 430072 Hubei Peoples R China Guoke Weiqiao Educ Grp Binzhou 256217 Shandong Peoples R China Hubei Res Ctr Language & Intelligent Informat Proc Wuhan 430072 Hubei Peoples R China Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Hubei Peoples R China

English video teaching is an indispensable part of English teaching, but there are certain difficulties in the design of English video courses, which need to be guided by artificial intelligence methods. Based on machine learning algorithm and sobel algorithm, this paper builds an English video classroom design platform, uses image technology to process video, and proposes an improved algorithm based on sobel operator. Moreover, this paper uses sobel horizontal and vertical detection operators to define the edge strength of the block to be repaired and introduces a weight factor to enable the improved algorithm to repair images with different texture levels. In addition, this article improves the original SSD by introducing STD based on the image standard deviation and uses the degree of gray value dispersion between image blocks as a criterion for comparing whether two image blocks are similar, which effectively improves the accuracy of the search strategy. The experimental results show that the algorithm constructed in this paper can play a certain effect in teaching.

关键词： Machine learning sobel algorithm English video Course design

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The Research of the Improvement of Iris Location of the sobel algorithm

The Research of the Improvement of Iris Location of the Sobe...

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2nd International Conference on Advanced Measurement and Test (AMT 2011)

作者： Ping, Li Shi Xin, Dong

ISBN: (纸本)9783037851975

An improved sobel algorithm is proposed for the iris location algorithm,solving the problem of large amount of computation in standard iris location algorithm as well as location impreciseness. According to the geometric character of human eyes for iris location,select purposely the special region of sub-image as the target region, then apply the edge detection,projection for rough location,then use the improved sobel algorithm to fine position. This improved sobel algorithm not only use the advantages of the original algorithm,but also make sobel algorithms only to detect circle. Improve the accuracy of iris location and make more accurate positioning. Experimental results showed that the algorithm with less computational can be accurately positioned inside and outside the boundary of the iris.

关键词： Iris Location Iris Recognition Edge Detection sobel algorithm

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BOHAI FRONT DETECTION BASED ON MULTI-SCALE sobel algorithm

BOHAI FRONT DETECTION BASED ON MULTI-SCALE SOBEL ALGORITHM

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Ping, Bo Su, Fenzhen Du, Yunyan Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9781479957750

In this paper, we introduce a multi-scale sobel algorithm for oceanic front detection. This algorithm takes advantage of the merit of large-scale sobel algorithm that can effectively decrease the influences of noises and the merit of small-scale sobel algorithm that can detect more details. Through the experiments, we can see that the proposed algorithm is suitable for front detection. On the basis of the proposed algorithm, we analyze the long-term variety of fronts in Bohai Sea.

关键词： front detection Bohai Sea sobel algorithm multi-scale edge detection

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Research on coal-rock boundary identification based on the morphological sobel algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Chen, Guohui Wang, Yilai Song, Shengwei Yang, Wenhua Heilongjiang Univ Sci & Technol Sch Mech Engn Harbin 150022 Peoples R China

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation;to a certain extent, subsequent intelligent mining is limited. A morphological sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

关键词： Coal-rock image Boundary recognition Morphology sobel algorithm

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An Improved sobel Face Gray Image Edge Detection algorithm 39

An Improved Sobel Face Gray Image Edge Detection Algorithm

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39th Chinese Control Conference (CCC)

作者： Tang, Xiaolin Wang, Xiaogang Hou, Jin Wu, Huafeng Liu, Dan Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Sichuan Peoples R China Artificial Intelligence Key Lab Sichuan Prov Zigong Peoples R China

ISBN: (纸本)9789881563903

In this paper, an improved sobel edge detection algorithm is proposed to overcome the shortcomings of traditional sobel edge detection operators, such as the limitation of detection direction in horizontal and vertical directions, and the need to set detection threshold artificially. Firstly, the detection direction is improved, based on the horizontal and vertical detection directions, two directions of 45 degree and 135 degree are added, which can detect the edge information of multiple gradient directions of the image. Secondly, considering the overall and local gray level of the input image, an edge judgment threshold is adaptively generated to make the detected image edge more complete. Finally, the multi-directional detection and adaptive threshold generation are combined. The experimental results show that the improved sobel edge detection algorithm can extract more direction edge information, and the edge boundary is clear, which has better robustness to noise interference.

关键词： sobel algorithm edge detection threshold generation self-adaption

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Comparative Analysis of Eight Direction sobel Edge Detection algorithm for Brain Tumor MRI Images

Comparative Analysis of Eight Direction Sobel Edge Detection...

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13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： A S, Remya Ajai Gopalan, Sundararaman Dept of Electronics and Communication Amrita Vishwa Vidyapeetham Amritapuri India

Brain Tumors are the leading cause of cancer death in children. They are caused by the abnormal and uncontrolled growth of cells inside the brain or spinal canal. Classification of brain tumors using machine learning technology is very relevant for radiologists to confirm their analysis more effectively and quickly. Segmentation algorithm identified for detecting the tumor from the MRI brain scans need to detect shapeless tumor growth perfectly. sobel edge detection is one of the widely used edge detection techniques in which only information along horizontal and vertical directions are considered. In this research, sobel algorithm with 8-directional template is implemented for improving the detection of edges in brain tumor MRI images. The proposed algorithm is compared with other traditional edge detection algorithms. The performance of the proposed algorithm is analyzed in terms of MSE, RMSE, Entropy, SNR and PSNR. Analysis shows that 8-sobel is comparatively the most suitable technique for analyzing brain tumor MRI images. Active contouring segmentation algorithm is applied on the edge detected images to verify the classification accuracy of segmented tumor. © 2022 Elsevier B.V.. All rights reserved.

关键词： Brain Tumor Edge Detection Eight-sobel algorithm sobel algorithm

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Comparative Analysis of Eight Direction sobel Edge Detection algorithm for Brain Tumor MRI Images

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Procedia Computer Science 2022年 201卷 487-494页

作者： Remya Ajai A S Sundararaman Gopalan Dept of Electronics and Communication Amrita Vishwa Vidyapeetham Amritapuri India

关键词： Brain Tumor Edge Detection sobel algorithm Eight-sobel algorithm

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An Improved sobel Face Gray Image Edge Detection algorithm

An Improved Sobel Face Gray Image Edge Detection Algorithm

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第三十九届中国控制会议

作者： Xiaolin Tang Xiaogang Wang Jin Hou Huafeng Wu Dan Liu School of Automation & Information Engineering Sichuan University of Science & Engineering Artificial Intelligence Key Laboratory of Sichuan Province

关键词： sobel algorithm edge detection threshold generation self-adaption

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