Particle swarm optimization in image processing of power flow learning distribution

Huang, Chuanchao

doi:10.1007/s43926-021-00012-9

Cited by 7 publications

(5 citation statements)

References 17 publications

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Section: Implementation Methods Based On Machine Vision-based Metallo...mentioning

confidence: 99%

“…Generally speaking, image processing technology uses mobile network terminals or other system hardware to perform some algorithmic processing on the electrical signals converted from image information to improve the practical usability of images. [24][25][26] Mahalanobis distance is a new method proposed by Indian scholars to measure the correlation of samples. The Mahalanobis distance makes up for the defect that the traditional Euclidean distance ignores the correlation between samples, and the Mahalanobis distance is better than the Euclidean distance in the image patch matching of edge and texture regions.…”

Section: Metallographic and Metallographic Analysismentioning

confidence: 99%

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(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Shi²,

Ke³

et al. 2022

J. Electron. Imag.

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. In recent years, with the development of computer image processing technology and quantitative metallography, quantitative metallographic analysis and image restoration based on machine vision image processing have attracted extensive attention. To deeply study the special ability of machine vision technology in metallographic image restoration and quantitative analysis, we adopt a model construction method, parameter design method and comparative analysis method to collect samples and analyze the machine vision system. Through quantitative metallographic organization, based on the progress in artificial neural networks and classification algorithms, a set of analysis system equipment and image restoration algorithm was finally established. The experimental results show that the recognition rate of this method is 5.69% higher than that of method 3, and the overall recognition rate is 5.1% higher than that of method 3, reaching 98.65%. It basically proves the superiority of the image classification algorithm in this paper and also indirectly proves that the system can play a role in the restoration and quantitative analysis of metallographic tissue images.

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Section: Implementation Methods Based On Machine Vision-based Metallo...mentioning

confidence: 99%

Section: Metallographic and Metallographic Analysismentioning

confidence: 99%

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Shi²,

Ke³

et al. 2022

J. Electron. Imag.

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“…After obtaining the observation value of the lung ultrasound image, to classify the observation value, it is necessary to design a suitable network to classify it. Various types of neural networks have been widely used in the field of image classification [ 10 – 13 ]. The application background of this article is a portable embedded ultrasonic testing equipment.…”

Section: Single-hidden-layer Neural Network Based On Bp Algorithmmentioning

confidence: 99%

Research on Lung Ultrasound Image Classification Based on Compressed Sensing

Wang

et al. 2022

Journal of Healthcare Engineering

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Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The commonly used X-ray, CT, and other diagnostic instruments are not suitable for rescue sites due to their large size, heavy weight, and difficulty in transportation. Ultrasound equipment is easy to carry and suitable for rescue environments. However, ultrasound images are noisy, have low resolution, and are difficult to get started, which affects the efficiency of diagnosis. This paper studies the effect of lung ultrasound image recognition and classification based on compressed sensing and BP neural network. We use ultrasound equipment to build a lung simulation model, collect five typical features of lung ultrasound images in M-mode, and build a dataset. Using compressed sensing theory, we design sparse matrix and observation matrix and perform data compression on the image data in the dataset to obtain observation values. We design a BP neural network, input the observations into the network for training, and compare it with the commonly used VGG16 network. The method proposed in this paper has higher recognition accuracy and significantly fewer parameters than VGG16, so it is suitable for use in embedded devices.

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“…Judging whether it is over, if so, the tracking is over, if otherwise, repeating steps (2) to (4). 18 The entire algorithm flow is shown in Fig. 4.…”

Section: Resamplingmentioning

confidence: 99%

(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

Jia

Wang

2022

J. Electron. Imag.

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.With the vigorous development of contemporary information technology, the field of computer technology is constantly innovating and progressing. Among them, the field of computer vision has achieved more prominent development. Computer vision is a discipline that uses cameras and computers to imitate the structure of human organs, processes and analyzes the collected video sequences, and then makes corresponding behavior judgments. Moving object detection and tracking is an important branch of computer vision. Moving target detection and tracking has broad application space and development scenarios. Here, we aim to study the detection and tracking method of moving targets in a martial arts arena video based on non-scene target features. Based on this research theme, we combine the particle filter moving target tracking algorithm based on non-scene target features to carry out the moving target detection and tracking experiments in the video of the martial arts arena. The experiment concluded: compared with the conventional methods, the particle filter moving target tracking algorithm based on the non-scene target features can perform the following tasks without affecting the detection accuracy. It reduces the time for detecting and tracking the same moving target in the video of the martial arts arena by 1.5 min. It shows that the algorithm can improve the efficiency of non-scene moving target detection and tracking.

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Particle swarm optimization in image processing of power flow learning distribution

Cited by 7 publications

References 17 publications

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Research on Lung Ultrasound Image Classification Based on Compressed Sensing

(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

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