NSST and vector‐valued C–V model based image segmentation algorithm

Wang, Xianghai; Zhao, Xianfeng; Zhu, Yihuan; Su, Xin

doi:10.1049/iet-ipr.2018.5027

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…To verify the effectiveness of the biometric image microfeature segmentation algorithm based on improved density peak clustering, experiments are carried out. The SSARPHS algorithm proposed by Reference [ 1 ], the SBP algorithm proposed by Reference [ 2 ], the SSFT algorithm proposed by Reference [ 3 ], the SNSSTCV algorithm proposed by Reference [ 4 ], and the SEIC algorithm proposed by Reference [ 5 ] are compared with the proposed algorithm, and the segmentation effects of the six methods are compared.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…The algorithm applies the BP neural network to color fundus image segmentation and uses adaptive histogram equalization, morphological processing, and the matched filtering algorithm to segment the image; Zhuang et al [ 3 ] proposed an ultrasonic image segmentation method based on the fractal theory and fuzzy enhancement. With this method, the ultrasonic image is enhanced by fuzzy technology, and then the image is segmented by enhancement technology; Wang et al [ 4 ] proposed an image segmentation algorithm based on the NSST and vector-valued model. The algorithm combines the NSST and vector-valued model and extracts multidimensional data from the image by using the sampling shear wave transform method to realize image segmentation; Reference [ 5 ] proposed a cell image segmentation method based on edge intensity cues.…”

Section: Introductionmentioning

confidence: 99%

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Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering

Sha

Liu

2022

Computational and Mathematical Methods in Medicine

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To address the problem of low precision in feature segmentation of biological images with large noise, a microfeature segmentation algorithm for biological images using improved density peak clustering was proposed. First, the center pixel and edge information of a biological image were obtained to remove some redundant information. The three-dimensional space of the image is constructed, and the coordinate system is used to describe every superpixel of the biological image. Second, the image symmetry and reversibility are used to obtain the stopping position of pixels, other adjacent points are used to obtain the current color and shape information, and more vectors are used to express the density to complete the image pretreatment. Finally, the improved density peak clustering method is used to cluster the image, and the pixels completed by clustering and the remaining pixels are evenly distributed into the space to segment the image so as to complete the microfeature segmentation of the biological image based on the improved density peak clustering method. The results show that the proposed algorithm improves the segmentation efficiency, segmentation integrity rate, and segmentation accuracy. The time consumed by the proposed biological image microfeature segmentation algorithm is always less than 2 minutes, and the segmentation integrity rate can reach more than 90%. Furthermore, the proposed algorithm can reduce the missing condition and the noise of the segmented image and improve the image feature segmentation effect.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering

Sha

Liu

2022

Computational and Mathematical Methods in Medicine

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“…5 Wireless Communications and Mobile Computing optimal threshold, you must cross all pixel values in the grayscale range and calculate the amount of change. When the amount of calculation is large, the output will be very low [26,27]. At the same time, due to the influence of factors such as the gray level of the image itself and the noise interference that has not been eliminated, the best limit cannot be reached by using only the gray histogram, which will lead to very unsatisfactory processing results [28].…”

Section: Filter Effect Evaluation and Results Analysismentioning

confidence: 99%

Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision

Xue

2021

Wireless Communications and Mobile Computing

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With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large ( 46.276 > 31.2271 ), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously ( 32.2271 < 33.3695 ), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.

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“…Step 2: the estimation of noise variance σ 2 is realized from different decomposition levels and directions [12].…”

Section: Microbial Image Denoising Based On Wavelet Transformmentioning

confidence: 99%

Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

Zhou¹

2022

Mathematical Problems in Engineering

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Image segmentation technology can effectively extract the foreground target in the image. However, the microbial image is easily disturbed by noise, its greyscale has the characteristics of nonuniform distribution, and several microorganisms with diverse forms exist in the same image, resulting in insufficient accuracy of microbial image segmentation. Therefore, a multithreshold microbial image segmentation algorithm using improved deep reinforcement learning is proposed. The wavelet transform method is used to remove the noise of the microbial image, the threshold number of the microbial image after denoising is determined by calculating the number of peaks of the grey histogram, and the foreground target of the microbial image is enhanced by the mean iterative threshold segmentation method, the preliminary segmentation of the microbial image is realized, the multithreshold microbial image segmentation model based on ResNet-Unet is constructed, and the cavity convolution and dual Q network mechanism are introduced to improve the segmentation model. The preliminary segmented microbial image is input into the improved segmentation model to realize the segmentation of the multithreshold microbial image. The results show that the proposed algorithm can effectively remove the noise of microbial images. With the increase in the number of thresholds, the peak signal-to-noise ratio, structural similarity, and feature similarity show an upward trend, and the loss rate of the model is less than 0.05%. The minimum running time of the algorithm is 3.804 s. It can effectively and quickly segment multithreshold microbial images and has important application value in the field of microbial recognition.

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NSST and vector‐valued C–V model based image segmentation algorithm

Cited by 9 publications

References 30 publications

Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering

Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering

Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision

Multithreshold Microbial Image Segmentation Using Improved Deep Reinforcement Learning

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