An image segmentation method using automatic threshold based on improved genetic selecting algorithm

Wang, Zhiwen; Wang, Yuhang; Jiang, Lianyuan; Zhang, Canlong; Wang, Pengtao

doi:10.3103/s0146411616060092

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Integrate the various small areas so that the steel plate defect target and background can be separated as a whole [34,35]. Uveitis is a chronic inflammation.…”

Section: Improved Threshold Segmentation Algorithm Based Onmentioning

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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“…Integrate the various small areas so that the steel plate defect target and background can be separated as a whole [34,35]. Uveitis is a chronic inflammation.…”

Section: Improved Threshold Segmentation Algorithm Based Onmentioning

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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“…In order to realize the full automation of the algorithm, it is first necessary to find the position of the heart in the image, so as to exclude the interference of other non-target area information based on the obtained position information, and further realize the segmentation of the heart. In [28], [29], the generalized Hough transform algorithm is used to realize the localization of the heart, but the generalized Hough transform has a large computational period and is not suitable for the positioning processing of CT image sequences. It is also based on heart shape and image gray, statistical methods for detecting prior knowledge such as degree characteristics, and a widely used method is a cascading method based on simple feature extraction.…”

Section: Heart Segmentation Algorithm Based On Convolutional Neural Networkmentioning

confidence: 99%

Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

2020

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The emergence of 4D heart images makes the data volume of the images multiply. It is more urgent to require an effective and fast segmentation algorithm. Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary. Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence. Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image. Convolutional neural network is a special deep neural network model of artificial intelligence. Its connections between neurons are not fully connected. The weights of connections between certain neurons in the same layer are shared, and the network model is reduced. The complexity reduces the number of weights. The use of visual saliency techniques to achieve cardiac segmentation based on CT images. An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique. In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved GrabCut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image. At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect. The convolutional neural network is constructed to realize the positioning function of the heart in the image. The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed. A stacking noise reduction self-coding network is constructed, and the network is manually segmented. Training, realize the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart, and finally realize the segmentation of the heart image based on the classification result. The results of the above segmentation algorithm are quantitatively evaluated and analyzed with the artificial segmentation results, and the segmentation results are visually reconstructed by surface rendering and volume rendering. The algorithm has better accuracy, reliability and higher. The segmentation efficiency is more simplified for user operations.

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Adaptive Threshold Processing of Secondary Electron Images in Scanning Electron Microscope

Bian

Wang

Yang

2019

Intelligent Robotics and Applications

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An image segmentation method using automatic threshold based on improved genetic selecting algorithm

Cited by 7 publications

References 14 publications

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

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

Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

Adaptive Threshold Processing of Secondary Electron Images in Scanning Electron Microscope

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