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Landscape reconstruction of traditional village couplets based on image recognition algorithm

JOURNAL OF OPTICS-INDIA 2023年第1期52卷 224-232页

作者： Wang, Xuan Jin, Xiaoli Feng, Yate Changsha Univ Sch Art Design Changsha 410022 Hunan Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Guilin Tourism Univ Sch Tourism Data Guilin 541006 Guangxi Peoples R China

Cultural inheritance, activation and utilization are the value orientation of the living protection of couplet landscape. In the reconstruction of traditional village couplet landscape, taking the repair of village cultural landscape gene as the starting point. The image recognition algorithm is used to comprehensively and systematically extract various information that can reflect the cultural connotation of the village in the original couplet landscape, find the appropriate couplet text from the database, match the calligraphy text and plaque style with high aesthetic feeling, and the optimal couplet landscape reconstruction scheme meeting the real needs is formed based on the overall perception of local residents on landscape gene repair, it is an effective strategy to realize the benign and coordinated development of traditional village cultural ecological activation and beautiful village construction.

关键词： Couplet landscape image recognition algorithm Cultural inheritance

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Dangerous Behavior image recognition algorithm of Smart Port Based on Deep Neural Network

Dangerous Behavior Image Recognition Algorithm of Smart Port...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Cui, Di Zhan, Xiaotiao Sun, Guoqing China Waterborne Transportation Research Institute Safety and Emergency Division Beijing China

ISBN: (纸本)9798350315455

In this paper, a new deep learning-based image recognition method is proposed to address the problems of existing image recognition methods. Through the depth neural network, the Generative adversarial network model is improved, so that the generated images can be classified, and the image recognition algorithm is optimized. And to verify the effectiveness of DNN in the application of image discrimination algorithm for dangerous behaviors in smart ports, this paper compares it with the traditional image recognition algorithm. The research results show that when the number of dangerous behavior images of Smart port is 500, it has been experimentally verified that its recognition accuracy can reach 94.40%, and the time required for image recognition is 0.29 seconds. This shows that the algorithm in this paper has high recognition accuracy, fast recognition speed, and can effectively identify dangerous behaviors in the port, which plays an important role in ensuring the safety of life and property of Smart port staff. © 2023 IEEE.

关键词： dangerous behavior deep neural network image recognition algorithm smart port

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Application Research of image recognition algorithm Based on Deep Learning in Intelligent Monitoring System 6

Application Research of Image Recognition Algorithm Based on...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Pang, Liang Kan, Guorui Hubei University of Science and Technology School of Economics and Management Hubei Xianning China Kaixuan Mingdi No. 7 Jing'an Road Wuchang District Hubei Wuhan China

ISBN: (纸本)9798350389418

With the extensive implementation of smart surveillance systems in the security domain, enhancing image recognition accuracy and efficiency has emerged as a crucial challenge. In this research, an image identification algorithm rooted in deep learning is proposed to boost the object detection and recognition capabilities of intelligent monitoring systems. During the system simulation phase, a convolutional neural network (CNN) model is established to train and test a substantial volume of surveillance image data. Comparative experiments validate the superior performance of the proposed method in complex environments. The findings demonstrate that this approach can greatly enhance the monitoring system's target identification precision across different scenarios while maintaining excellent processing speed. The in-depth analysis of simulation data further confirms the reliability and practicality of the system, offering technical backing for the evolution of intelligent monitoring systems. This work not only details the algorithm design process but also validates the simulation outcomes through precise data analysis, indicating that this algorithm's application in smart surveillance systems holds broad prospects and significant practical value. © 2024 IEEE.

关键词： Deep learning image recognition algorithm intelligent monitoring system system simulation

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Fundamental Research on image recognition algorithm based on Convolutional Neural Network

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第8期7卷 90-93页

作者： Lihua Luo

image recognition technology has been widely used in various fields. The research on image recognition has been carried out for many years. In the existing algorithms, there are mainly two categories. One is the traditional image recognition algorithm based on image processing, and the other is the new image recognition algorithm based on artificial intelligence. Traditional image recognition algorithms take a long time, cannot achieve real-time processing, and have low accuracy. Compared with the former algorithm, the image recognition algorithm based on artificial intelligence is simpler and faster. It can learn more advanced image features, thereby improving the accuracy of image recognition, so it is widely used. This paper focuses on image recognition algorithms based on convolutional neural networks, and proposes image recognition algorithms based on artificial neural networks with low complexity, high recognition accuracy, high efficiency, and fast real-time. Various algorithms are analyzed, and the application characteristics of each type of algorithm are summarized.

关键词： image recognition algorithm Convolutional Neural Network Various algorithms based on Artificial Intelligence Basic Research

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Semi-supervised Human-Robot Interactive image recognition algorithm

Semi-supervised Human-Robot Interactive Image Recognition Al...

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International Congress on image and Signal Processing

作者： Hong Zhang Ping Wu State Key Laboratory of Software Engineering Wuhan University College of Computer Science and Technology Wuhan University of Science and Technology

ISBN: (纸本)9781467390996

image semantics recognition is a long-standing research topic and has been used to many application areas, including medical diagnose, public security, etc. However, how to teach a social robot to have the intelligence to recognize images through user interactions still remains open and ambitious. In this paper we propose a novel framework of semi-supervised human-robot interactive image recognition. In our framework, the user first presents unlabeled images to a humanoid robot for recognition;then the robot answers the user what the image is based on a semi-supervised learning algorithm;thirdly if the robot's answer is wrong, the user correct the robot with the right label. With the learning process going on, the robot is trained to recognize more and more images with different semantic labels. The ability of "learning image semantics" makes the user feel that the robot is more like an "intelligent life". Extensive experiments and comparisons have proved the efficiency of our framework with encouraging results.

关键词： image semantics understanding Social robot Semisupervised learning Human-robot interaction Semi-supervised Learning Robot Human machine interaction images image recognition algorithm imagery (Psychotherapy) Humanoid robots image recognition Interactive Frameworks Learning processes program area

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Research of image recognition algorithm Based on Depth Learning

Research of Image Recognition Algorithm Based on Depth Learn...

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2018 4th World Conference on Control, Electronics and Computer Engineering (WCCECE 2018)

作者： Zhang Jian Ji Xinhao Zhejiang Business College

In recent years,deep learning has become more and more widely used in the fields of speech recognition,computer vision and *** in the field of image recognition,deep learning-based convolutional neural networks show a strong technical superiority that surpasses previous machine vision *** traditional methods of machine vision have not been able to meet the practicality and security requirements of image recognition in the big data *** this stage,deep learning has become a hotspot in image recognition *** this end,the paper elaborates the background and basic ideas of deep learning,and analyzes common models of deep learning:deep belief networks,algorithm principles of convolutional neural networks,and image recognition efficiency.

关键词： Depth Learning image recognition algorithm Deep Neural Network Convolution Neural Network

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Exploration and Optimization of image recognition algorithms for Smart Port Security Monitoring System

Exploration and Optimization of Image Recognition Algorithms...

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作者： Cui, Di Zhan, Xiaotiao Sun, Guoqing Safety and Emergency Division China Waterborne Transportation Research Institute Beijing China

With the development of international maritime trade, the actual number of surveillance cameras in smart ports is increasing. In response to the widely valued early warning detection and prevention system, the security monitoring optimization system based on image recognition technology can effectively cope with the increasingly complex real-time monitoring system. This article studies the design of an improved security monitoring system using image recognition technology. After preprocessing and feature extraction of image data, combined with the evaluation of security supervision engineers and testing samples of important port areas, the following conclusions are drawn through questionnaire survey and simulation experiment analysis: Within the framework of intelligent security systems, security monitoring systems that utilize image recognition technology surpass traditional monitoring methods in accuracy across all important area samples, with an average improvement of 6.4%. They also yield favorable practical results in system stability, with an average satisfaction rating of approximately 91.2 points. This research offers a more efficient and precise monitoring method for port security management, substantially enhancing the safety of goods, equipment, and personnel, while also reducing the costs and error rates associated with manual monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image recognition algorithm Security monitoring system Simulation experiment Smart port

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A comparison of image recognition algorithms for cell phenotyping in optofluidic time-stretch microscopy 4

A comparison of image recognition algorithms for cell phenot...

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Conference on Real-Time Photonic Measurements, Data Management, and Processing IV

作者： Zhao, Wanyue Lei, Cheng Huang, Honghao Goda, Keisuke Chen, Hongwei Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430072 Peoples R China Univ Tokyo Dept Chem Tokyo 1130033 Japan Japan Sci & Technol Agcy Kawaguchi 3320012 Japan

ISBN: (数字)9781510631021

ISBN: (纸本)9781510631021

Optofluidic time-stretch imaging system has enabled high-throughput phenotyping of cells with unprecedented high speed and resolution. However, significant amount of raw image data is produced, which requires recognition algorithm with not only high accuracy but also high speed to analyze image data efficiently. In this paper, we compare the performance of popular feature extraction methods and learning-based classification algorithms on time-stretch microscopy image recognition. The applied image recognition system comprises an outlier detection step, feature extraction method and classification. The main concept of outlier detection uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to eliminate error images. Gabor wavelet, HOG (Histograms of Oriented Gradients), LBP (Local Binary Pattern) and PCA (Principal Components Analysis) are applied and compared as the feature extraction methods. Finally, with a set of extracted features, the computing time and accuracy of SVM (Support Vector Machines), LR (Logistics Regression), ResNet (Residual Neural Network) and XGBoost (Extreme Gradient Boosting) classification algorithms are evaluated. The tested cell image datasets are acquired from high-throughput imaging of numerous drug-treated and untreated cells (N = similar to 21,000) with an optofluidic time-stretch microscope. Results show that PCA feature extraction and XGBoost classification proves to be the fastest algorithms with the highest level of accuracy. DBSCAN outlier detection helps to improve the recognition accuracy by 2% approximately. Therefore, we propose a recognition algorithm consisting of DBSCAN outlier detection, PCA feature extraction and XGBoost classification as a promising solution to process the image data of high-throughput optofluidic time-stretch microscopy accurately and rapidly.

关键词： time-stretch cytometry image recognition algorithm

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image recognition for gastrointestinal disease detection and diagnosis in QoS and QoE evaluation of 5G network communication

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SOFT COMPUTING 2022年第24期26卷 13799-13813页

作者： Zhang, Wenxing Gao, Yanpeng Song, Shunyao Gao, Fangkai Weifang PeopleHosp Dept Gen Surg Weifang 261041 Shandong Peoples R China

With the improvement of people's requirements for the accuracy of disease detection, increasing researchers apply image recognition to the detection and diagnosis of gastrointestinal diseases. In addition, the evaluation of detection and diagnostic accuracy is also an important factor in the diagnosis and treatment of gastrointestinal diseases. Referring to the QoS and QoE evaluation scheme in 5G network communication, this paper proposes a secondary quality experience evaluation model for gastrointestinal diseases. Firstly, three mandatory detection implementation nodes of 5G network communication are created in the image recognition model, and the first node detects the detected points (images of intestinal diseases) in 5G network communication in the form of interactive information. Then, take the above detection results as the first level to obtain the quality detection strategy results, and the second node uses the policy detection server to evaluate the measurement results as the results of the second level quality evaluation strategy. Finally, the third node is used to evaluate the results of the secondary quality evaluation, and the evaluation results of gastrointestinal disease detection are returned to the output of the whole model. In this paper, 200 CT images of the gastrointestinal tract in the CT colonography dataset were used to evaluate the performance of the model. The study shows that after 200 gastrointestinal CT images are imported into the model, 173 samples have a score of more than 85% for the output of other nodes at the end of the enforcement node, accounting for 86.5% of the sample size. After 24 samples were evaluated by the second level, the output result was the first level scoring error, accounting for 12% of the sample size. Three samples were judged as abnormal images by the first level enforcement node, accounting for 1.5% of the sample size. From the results, the secondary quality experience evaluation model can improve the image re

关键词： Disease detection accuracy Gastrointestinal disease detection and diagnosis image recognition algorithm 5G Network communication QoS and QoE evaluation

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FUZZY-BASED HISTOGRAM EQUALIZATION PROCEDURE FOR image recognition USING POINTED SET

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JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY 1995年第5期39卷 457-463页

作者： YOO, SW GIARDINA, CR CUNY COLL STATEN ISL DEPT COMP SCISTATEN ISLNY 10314

An image recognition algorithm using a fuzzy-based histogram equalization procedure is developed. The equalization procedure is applied to two histograms, one of regions and the other of intensities, of the input image. Fuzzy logic is applied in equalizing these histograms, and a revised version of the image is obtained. The revised image is then used in the image-recognition algorithm, which allows a permissible intensity difference in comparison with the archetype image.

关键词： histograms image recognition algorithm image image recognition Equalization

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