Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing

Kong, Wei-Bin; Chen, Jianzhao; Song, Yongchen; Fang, Zhongqing; Yang, Xiaoguang; Zhang, Hongyan

doi:10.14569/ijacsa.2023.0140266

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…During these processes, Canny and Sobel detection techniques and more accurate determination of the edges [30][31][32] and the effect of providing threshold values were also examined. Due to the nature of the algorithms used [30,31], two threshold values, Low and High, in the interval 0<Low<High<1 for Canny [33,34] and the single threshold value for Sobel [35] has been applied. After all the different threshold values were applied, the morphological operator determined the edges.…”

Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

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Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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“…These feature maps contain different raw edge information. Finally, in order to enhancing the edge feature extraction ability of the model, Sobel operator [16] is used to extract the edge features from feature maps A 2 , A 3 , A 4 , A 5 , and these edge features are concatenated together to get the final output A 6 .…”

Section: The Sobel MIX Cross Poolingmentioning

confidence: 99%

MRHCNet: Multi Resolution Hybrid Convolution based U-Net for Crack Detection with Mix Dilated Linear Pooling and Laplacian Pyramid Mix Module

Zhang,

Huang

2023

Preprint

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Deep learning based methods have achieved the state-of-art results in crack detection. However, these models have some drawbacks. Firstly, these models often use down-sampling operation to reduce the resolution of features, which would cause feature information loss. Secondly, square filtering windows are used to extract strip crack features, which would merge unrelated feature information. Thirdly, these models are limited to learn detailed feature information and edge features of cracks, which would cause detail and edge feature information loss. To solve these problems, a Multi Resolution Hybrid Convolutional Module (MRHCM) is designed to avoid feature information loss caused by down-sampling. Additionally, a Mix Dilated Linear Pooling (MDLP) is proposed to capture the strip feature information of cracks. Furthermore, a Laplacian Pyramid Mix Module (LPMM) is designed to learn detailed feature information of cracks. Also, a Sobel Mix Cross Pooling (SMCP) is used to enhance the network?s ability of learning edge features. Finally, a Multi Resolution Hybrid Convolution based U-Net (MRHCNet) with MDLP, LPMM and SMCP is proposed by us. Experimental results show that our MRHCNet could achieve the accuracy of 0.931, 0.825 and 0.989 on the Cracktree200, CRACK500 and CFD datasets respectively, which is higher than that of the traditional models.

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“…This method is unable to detect the detail region edge information, there is still some room for progress. Kong et al [24] applied the improved genetic algorithm to different images to obtain different Sobel edge detection thresholds, and the obtained edge continuity is better. However, the noise immunity is poor and the detection accuracy is not high.…”

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confidence: 99%

Edge Detection of High-Resolution Remote Sensing Image Based on Multi-Directional Improved Sobel Operator

Shi,

Zhao

2023

IEEE Access

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Automatic detection of building edge information from high-resolution remote sensing images can more accurately obtain building distribution information, which is of great significance for urban land planning, urban building planning and population estimation. The satellite image will be affected by atmospheric interference and noise, we preprocess the image to eliminate the atmospheric interference such as atmospheric correction and radiometric calibration, and utilize a new total variation and wavelet adaptive thresholding hybrid filter proposed in this paper to achieve the filtering of noise while retaining the image edge detail information. The traditional detection operator is prone to the problems of scattered edge points, discontinuity or misjudgment of too many edge points in the detection results due to the limitations of directions, template sizes. Therefore, this paper proposes a new 16-direction 5  5 size Sobel operator to replace the previous Sobel operator with only horizontal and vertical directions. These 16 directions are utilized to calculate the gradient of each pixel points. The templates for each direction are extracted for weighted fusion into the contour image for each direction. Thus, the edge information of different angles can be extracted, and the all-round information edge extraction of the image can be realized. By comparing the experimental visual effect and the evaluation criteria of the data results, the SNR, PSNR, AUC and FOM values of the improved algorithm are much higher than other algorithms, with the advantage of higher image detection edge positioning accuracy, more complete contour lines and stronger anti-interference ability.

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Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing

Cited by 11 publications

References 12 publications

Machine Whell Edge Detection Morphological Operations

Machine Whell Edge Detection Morphological Operations

MRHCNet: Multi Resolution Hybrid Convolution based U-Net for Crack Detection with Mix Dilated Linear Pooling and Laplacian Pyramid Mix Module

Edge Detection of High-Resolution Remote Sensing Image Based on Multi-Directional Improved Sobel Operator

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