Intelligent Image Recognition Research Articles

Remote classroom action recognition based on improved neural network and face recognition

In recent years, the field of computer vision is promoted by the development of intelligent technology and computer technology, and has made breakthrough progress. Intelligent hardware technology and computer technology lay the foundation for the development of computer vision field. At the same time, the continuous improvement and development of artificial intelligence technology has also promoted the rapid development of educational video system, and the video tracking of educational video system has made breakthrough progress. By fully using intelligent hardware and computer technology, and combining with artificial intelligence technology, the video tracking and recognition technology of educational video system has been further developed, and new recognition algorithm has been adopted. The accuracy of tracking recognition is greatly improved, which can accurately identify the action of the characters. At the same time, through the use of new action recognition algorithm, not only improve the accuracy of educational video recognition, but also improve the speed of recognition, which can accurately capture the changes of people’s behavior in the classroom. The time consumed by the action recognition algorithm is very short, and the speed of the algorithm is very high. This new algorithm greatly improves the efficiency of the education recording and broadcasting system, and improves the accuracy and accuracy of the education recording and broadcasting system. This paper studies a set of intelligent image recognition system for students’ classroom behavior. It compiles and explains the intelligent system software systematically. The operation of this system is no single. It operates through the joint operation of many modules. It can realize online distributed homework, accurately and quickly identify students’ classroom behavior, and can also help students to identify their classroom behavior accurately and quickly. The classroom behavior of the accurate analysis of students’ incorrect classroom behavior to make timely reminders, greatly improve the efficiency of the classroom, improve the degree of concentration of students. In this paper, many classroom behaviors are simulated, and the performance of this software platform is predicted through many experiments.

Tennis Robot Design via Internet of Things and Deep Learning

To efficiently integrate deep learning (DL) and Internet of Things (IoT) with tennis robot research, the concept and characteristics of mask region-convolutional neural network (Mask R-CNN) under DL are analyzed. Then, the IoT radio frequency identification (RFID) is introduced and the suitable RFID structure is established. Moreover, the real-time intelligent image recognition tennis robot is designed, and simulation experiment on the robot is performed. The results show that when the positioning label is eight, the convergence speed of the optimized algorithm is improved relative to the unoptimized one, and the error is reduced by 0.82. The positioning algorithm proposed in this research has high convergence speed and small system error. The positioning accuracy of the tennis robot is improved by at least 6%, the positioning targets are close to the target to be located, and the tennis robot has the best shortest path effect. In addition, the tennis recognition algorithm based on Mask R-CNN can accurately distinguish dense tennis balls with high accuracy. It shows that the tennis robot positioning algorithm and target recognition algorithm proposed in this research are of practical adoption value.

Research on intelligent recognition and encryption algorithm of network big data image

In view of the identification and encryption of massive network information in the era of big data, this paper proposes to use intelligent image recognition technology to analyze the required information, then classify it according to the big data clustering method, and use pseudo pixel encryption algorithm for multiple encryption. The simulation results show that the algorithm has high recognition rate, good encryption effect, strong anti-interference ability and good ability to protect network image information.

Design of Home Care System For Rural Elderly Based on Artificial Intelligence

With the development of Internet of Things technology, cloud computing technology, big data technology, and artificial intelligence technology, the application of these new technologies to rural life can help rural elderly care at home. The home care system for the elderly in rural areas is based on artificial intelligence technology and uses the Internet of things technology to collect real-time environmental parameters at home and to optimize the living environment. It uses intelligent image recognition technology to judge the behavior of the elderly and interlopers and gives effective early warning and alarm. It uses wearable devices to measure the health of the elderly in real-time. It uses the man-machine dialogue program to solve the life needs of the elderly to improve the well-being of the elderly life. It integrates big data and cloud computing technology to process the collected information.

Research on Intelligent Image Recognition Technology Based on Equalization Algorithm

In the image edge distortion correction, the straight-line projection-derived edge fit is poor, resulting in large correction errors. Therefore, an image edge distortion correction algorithm based on the equalization algorithm is proposed in the paper, where the adaptive threshold wavelet denoising algorithm is adopted to implement adaptive transformation of various scale parameters to complete noise removal. According to the results of image denoising, Fisher vector coding is used to optimize the results of the equalization algorithm to extract the edge distortion of the image. Besides, based on the edge distortion shape extraction results, the correction objective optimization function is obtained to analyze the edge fracture situation, realizing the linear projection derivative edge fitting. Meanwhile, by determining the image edge error evaluation function, the method of image edge distortion correction is judged to achieve the purpose of image edge distortion correction. Therefore, taking noisy barrel distortion and pincushion distortion images as the research object, the experimental analysis is carried out. The results show that the algorithm proposed in the paper can effectively correct the barrel distortion and pincushion distortion of the image, achieving high-precision and high-efficiency image edge distortion correction.

Guest Editorial Introduction to the Special Section on Intelligent Visual Content Analysis and Understanding

Visual content analysis and understanding attract tremendous attention because of its potentially wide range of applications including human activity analysis, automated photo face tagging, multicamera tracking, crowded counting, and biometric security. With recent progress in end-to-end differentiable learning, the accuracy of algorithms has been significantly improved and even outperforms humans in some tasks. In addition, multimodality methods, targeting on making full use of various visual data sources, are further investigated. These developments contribute to the innovations of two core modules for a typical intelligent vision system, i.e., image and video description and recognition, which are critical for the success of the visual content analysis and understanding in more complex and challenging open world.

A research on an intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties

The intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties—the “Shang Zhou Bronze Inscriptions Intelligent Mirror”—was successfully invented in Shanghai. This mirror, based on the computer technology of artificial intelligence (AI) image recognition and image retrieval, succeeds in automagical recognition of bronze inscriptions, both single letters and full texts. This research leads the trend of the AI recognition of Ancient Chinese characters and accumulates valuable experience for the development of inter-disciplinary research on Chinese character recognition. This essay emphasizes the importance of the bronze inscriptions of the Shang and Zhou dynasty database in the AI recognition of bronze inscriptions, introduces the functional components of this tool, and shares the whole research process in order to offer experience for the related research on AI recognition of other types of Ancient Chinese characters as well as ideographs in the world scope. “Shang Zhou Bronze Inscriptions Intelligent Mirror” as a tool for bronze inscription recognition also has room for improvement and support, and guidance from experts in similar areas is greatly welcomed.

The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning

This article aims to improve the accuracy of real-time image recognition in the context of the Internet of Things (IoT), reduce the core network pressure of the IoT and the proportion of IoT broadband, and meet people’s demand for internet image transmission. An intelligent image fusion system based on mobile edge computing (MEC) and deep learning is proposed, which can extract the features of images and optimize the sum of intra-class distance and inter-class distance relying on the hierarchical mode of deep learning, and realize distributed computing with the edge server and base station. Through comparison with other algorithms and strategies on the text and character data sets, the effectiveness of the constructed system is verified in the performance of the algorithm and the IoT. The results reveal that the application of the unsupervised learning hierarchical discriminant analysis (HDA) has better accuracy and recall in various databases compared with conventional image recognition algorithms. When the sum intra-class and inter-class distance K is 2, the accuracy of the algorithm can be as high as 98%. The combination of MEC and layered algorithms effectively reduces the pressures of core network and shortens the response time, greatly reduces the broadband occupancy ratio. The performance of IoT is increased by 37.03% compared with the general extraction and common cloud computing. Image recognition based on the MEC architecture can reduce the amount of network transmission and reduce the response time under the premise of ensuring the recognition rate, which can provide a theoretical basis for the research and application of user image recognition under the IoT.

Retracted: Image Recognition Technology Based on Machine Learning

With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects.

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

Wind Turbine Blade Defect Image Acquisition System

The detection of wind turbine blades in service is a difficult problem in wind power operation and maintenance. Wind farms in the quality assurance unit and daily inspection, regular detection of blade is with the aid of SLR camera, then the human recognition, low accuracy and low recognition efficiency. This paper puts forward a set wind turbine blade detection method based on based on artificial intelligence image recognition system. With the help of high definition camera array and image recognition software, a large number of image acquisition and rapid recognition processing are carried out for wind turbine blades in service, and finally the automatic detection of blade surface defects is realized. The feasibility and applicability of this method are verified through field detection, which can be promoted in the field of wind power.

Retracted: Consumer Marketing Brand Cultivation Path Based on Image Recognition Technology

In recent years, with the improvement of national income levels, people's consumption concept has begun to transition from enjoyment to quality, and people pay more and more attention to food safety and health. The research of intelligent brand image recognition technology is the most important to prevent the spread of counterfeit and shoddy products and brand cultivation. This article mainly studies the cultivation method of consumer marketing brand based on image recognition technology. This article is based on image recognition technology. Consumers can obtain relevant information and brand culture through the mobile phone camera function anytime, anywhere, establish a bridge between consumers and company brands, broaden the company's channels for obtaining consumer information big data, and deeply tap consumer potential. Demand, further enrich the brand cultivation methods, and achieve a new terminal service experience of "efficient, convenient and satisfactory" for consumers. In the experiment of this paper, the shape of the product packaging is relatively irregular, so there are fewer feature points generated in this part, which leads to the inaccurate extraction and distribution of visual words, which reduces the accuracy of single product extraction slightly, but the overall result is good. The maximum number of iterations in this experiment is 40, which can meet the requirements of most pictures. From the perspective of consumer perception, this paper explores and proposes a new path for consumer marketing brand cultivation of image recognition technology, promotes the deep integration of new artificial intelligence technology and brand industry, and fills the gaps in industry-related technologies and applications.

A Method of DC Arc Detection in All-Electric Aircraft

Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.

Research on elevator hydraulic buffer detection system based on intelligent image recognition

As a kind of vertical transportation device, elevator is widely used in the production field and daily lives. In order to detect the reset time of the elevator hydraulic buffer, this article first analyzes the shortcomings of the existing detection methods, then provides a design of detection system for the elevator hydraulic buffer based on intelligent image recognition. This system can detect the reset time and reset distance, and grasp the dynamic compression and reposition process of the elevator hydraulic buffer. Besides, this system can also bring lots of advantages, such as wide universality, easy installation, high degree intelligence, less influence of human factors and no risk for the inspectors in measurement process.

Data Retrieval System of JMA Analog Seismograms in the Headquarters for Earthquake Research Promotion of the Japanese Government

Abstract To archive precious analog seismograms of major earthquakes that occurred in and around Japan, we have scanned selected analog seismograms from 113 stations deployed by the Japan Meteorological Agency (JMA) and are constructing a database of the scanned digital images accessible through the website of the Headquarters for Earthquake Research Promotion (HERP) of the Japanese government. This project started in 2006 and was conducted by the Association for the Development of Earthquake Prediction. The database of HERP now contains more than 185,000 digital images of seismograms recorded during the period from 1884 to 1995. It includes seismograms of earthquakes that occurred in and around Japan and of some major teleseismic earthquakes. The oldest analog seismogram was recorded on 2 January 1884 at a station in Tokyo. The seismograms are the intellectual property of mankind, and the database of these digital images will be an important material for studies of past destructive earthquakes. Technological progress such as artificial intelligence image recognition technology will help to further explore the data.

The Design of Intelligent Transportation Video Processing System in Big Data Environment

The intelligent transportation system in big data environment is the development trend of future transportation system, which effectively integrates advanced information technology, data communication transmission technology, electronic sensor technology, control technology and computer technology and is applied to overall ground transportation management. Hence, it establishes a real-time, accurate, efficient and comprehensive transportation management system that functions in a wide range and all-round aspects. In order to meet the demands of the intelligent transportation big data processing, this paper puts forward a high performance computing architecture of large-scale transportation video data management based on cloud computing, designs a parallel computing model containing the distributed file system and distributed computing system to solve the problems such as flexible server increase or decrease, load balancing and flexible dynamic storage increase or decrease, computing power and great improvement of storage efficiency. On the basis of this technical architecture, the system adopts BP neural network-related algorithms to extract the static transportation signs in road videos, and uses interframe difference algorithm and Gaussian mixture model (GMM) fusion algorithm to extract the moving targets in road transportation videos. In this way, they are taken as important integral parts and data sources of key frames of intelligent video image recognition to improve the recognition ability of key frames and eventually utilize semantic recognition model based on CNN (Convolutional Neural Network) to complete the intelligent recognition of whole transportation videos. Through network pressure test, computing ability test, recognition ability test and other tests, it has been proved that the intelligent transportation video processing system based on big data environment is successful and the design scheme of this system has strong practical application value.

Prelaunch Matching Architecture for Distributed Intelligent Image Recognition

The paper presents a multi-agent solution for dynamic combination of several artificial neural networks used for image recognition. As opposed to the existing methods there is introduced a dispatcher agent that provides prelaunch matching of possible pro-active identification algorithms through competition. The proposed solution was implemented to solve a problem of stream processing of photo images produced by a number of distributed cameras using an intelligent mobile application. It was probated and utilized in practice to capture the results of electrical meters that are manually monitored by a group of patrol personnel inspectors using hand held devices. Prelaunch matching architecture allowed increasing the quality of digits recognition using various neural networks depending on the operating conditions.

Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections. In order to improve the convergence speed and recognition accuracy of the convolutional neural network algorithm, this paper proposes a new convolutional neural network algorithm. First, a recurrent neural network is introduced into the convolutional neural network, and the deep features of the image are learned in parallel using the convolutional neural network and the recurrent neural network. Secondly, according to the idea of ResNet's skip convolution layer, a new residual module ShortCut3-ResNet is constructed. Then, a dual optimization model is established to realize the integrated optimization of the convolution and full connection process. Finally, the effects of various parameters of the convolutional neural network on the network performance are analyzed through simulation experiments, and the optimal network parameters of the convolutional neural network are finally set. Experimental results show that the convolutional neural network algorithm proposed in this paper can learn the diverse features of the image, and improve the accuracy of feature extraction and image recognition ability of the convolutional neural network.

Intelligent system of English composition scoring model based on improved machine learning algorithm

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

A hybrid eye movement feature recognition of classroom students based on machine learning

Online classroom teaching is difficult to identify students’ learning status in real time. Therefore, we need to combine intelligent image recognition technology to analyze student status through eye movement features. This study solves the problem of inaccurate positioning of the initial position of the shape model in the process of eyelid matching through machine learning. Moreover, this study improves the algorithm and uses the AK-EYE model based on the combination of ASM algorithm and Kalman filtering to establish a local feature model for each feature point. According to the gray information in the normal direction of the feature point, the local gray information is modeled. After training through the sample set to obtain the state model, the target eye can be searched, and the pose parameters can be determined. Finally, this study designs a control experiment to analyze the performance of the model proposed in this study. The research shows that the algorithm proposed in this paper has a high recognition accuracy and has a practical basis, which can be used as one of the subsequent classroom teaching system algorithms.