Memristive network-based genetic algorithm and its application to image edge detection

Yu, Yongbin; Yang, Chenyu; Deng, Quanxin; Tashi, Nyima; Shouyi, Liang; Zhou, Chen

doi:10.23919/jsee.2021.000091

Cited by 22 publications

(6 citation statements)

References 6 publications

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“…To improve the effect of edge detection, the traditional ant colony algorithm is applied to image edge detection. The literature [ 51 ] combined the fast global search ability of genetic algorithms with the global convergence ability of ant colony algorithms and proposed an image edge detection research algorithm based on the genetic ant colony hybrid algorithm. It can improve the quality of image edge detection and greatly shorten the detection time.…”

Section: Swarm Intelligence and Its Application In Image Processingmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cao

et al. 2023

Biomimetics

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

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Section: Swarm Intelligence and Its Application In Image Processingmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cao

et al. 2023

Biomimetics

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“…There is a lot of effective information, and it is the most basic feature of an image. It is specifically manifested in sudden changes in texture structure, color, and gray level, that is, where the signal mutation occurs [12]. At present, there are two kinds of edge detection, namely color detection and gray detection, and the latter is used in most cases [13].…”

Section: Edge Detection Algorithmmentioning

confidence: 99%

Condition Monitoring and Fault Diagnosis of Engineering Ship Power Machinery Based on Edge Detection Algorithm

2020

KME

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The research of marine engine condition monitoring and fault diagnosis technology has developed for many years. For this reason, the engine fault diagnosis technology has been paid more and more attention by engine manufacturers, and has been taken as an important means to improve the engine operation reliability and reduce the use cost. This paper first introduces the traditional edge detection algorithm, which mainly uses different local operators to detect the acquired data images, and mainly compares and analyzes the detection results of Sobel operator and Canny operator of the first order local derivative. The architecture of the ship structure health monitoring system is designed, and the functional module of the edge computing node is divided into three layers and four functional modules, which respectively realize the collection, processing, storage and release of monitoring data. The module design adopts the software mode of weak coupling and strong cohesion, and establishes the standardization of data transmission format.

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“…According to the three categories of image segmentation, the most commonly used segmentation techniques are the threshold method, edge detection method, and region improvement method. In threshold segmentation, the maximum threshold and blur threshold are more suitable for the segmentation of specific types of images (vessels) [7][8]. Although that which separates the background from the target is not very obvious, the segmentation effect is not very good; the method of edge detection is to first detect the pixels along the edge of the image, and then stitch the end pixels together to form a segmented area.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Recognition Technology of Library Books Based on Image Processing

Xun

2024

IJCAI

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The intelligence of computers is the future development direction. In today's society, the amount of information is increasing, which puts forward higher requirements for retrieval technology and automation levels. With the development and popularization of the Internet era, online shopping, study, and even work have become the norm in people's lives. However, a large amount of data is generated on the Internet every day, and how to obtain the information people need from this data is a key research problem today. It can be seen that the traditional global search or image search can no longer meet the amount of information people need, so the content-based search method will inevitably become a more popular database retrieval technology. In recent years, content-based image capture has become a research center in the field of image information removal. This article first studies image processing and capture technology. It provides a detailed overview of image segmentation technology and image segmentation models introduced in image processing, as well as three image segmentation methods. This article also introduces the basic principles and framework of the CBIR procurement system. The most important technology in the CBIR system is the analysis and description of image features, and several methods commonly used to express the content of feature images. Secondly, it analyzes the image edge detection algorithm in detail and finally introduces the functional division and workflow of the library book automatic recognition system. It also provides all the construction environments, processes, and algorithms to perform the basic functions of the system. Povzetek: Predstavljena je tehnologije obdelave in zajemanja slik, podrobno še segmentacija slik in metode segmentacije.

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Memristive network-based genetic algorithm and its application to image edge detection

Cited by 22 publications

References 6 publications

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Condition Monitoring and Fault Diagnosis of Engineering Ship Power Machinery Based on Edge Detection Algorithm

Research on Automatic Recognition Technology of Library Books Based on Image Processing

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