Retracted] Sports Action Recognition Based on Image Processing Technology and Analysis of the Development of Sports Industry Pattern

With the rapid development of modern society and economy, various types of science and technology have also achieved relatively rapid development, which has led to significant changes in all walks of life in modern society. This change not only makes modern society transition toward an information society but also makes people in modern society obtain more convenience. The construction industry has also achieved higher quality development with the development of science and technology, especially through the informatization reform of existing technical means in multiple processes such as building design and construction, which has saved a lot of manpower and material resources. The most important aspect in the field of architecture is the design work before construction, and the degree of refinement in this process also determines the merits of the building to a certain extent. Therefore, the field of architectural design has also received more attention from relevant researchers. At the same time, the further development of social economy in the new era also puts forward more requirements for architectural design in the construction industry, which urges researchers to conduct in-depth research on existing architectural design. At the same time, combining some emerging information technologies, a new architectural design mode with better structure and performance is proposed. Image processing technology mainly uses computer algorithms to collect images, thereby converting these images into digital signals that can be recognized by a computer, and then displaying them on a computer display. This image processing technology can also identify and extract information from images, thereby displaying the key information therein. This article mainly analyzes image processing techniques and some data analysis algorithms to obtain the feasibility of their application in visual information mining systems for architectural design. The contribution of this study is to propose a new architectural design visual information mining system based on image processing technology and data analysis algorithms, and conduct experimental studies to prove that it is superior to existing systems in terms of information extraction accuracy, processing speed, and user convenience. The experimental results show that compared with the existing architectural design data mining system, the new system has an average performance improvement of 25.9% in many aspects, which marks an important step forward in the field of architectural design informatization. In addition, this study also provides a new perspective for the construction industry, i.e., applying image processing and data analysis technology to the design process to improve design quality and efficiency.

The rapid development of image and video processing technology has a significant impact on various industries. The analysis and forecast of the financial market income can not only provide investors with investment decisions, but also can correctly formulate various economic control policies for the government. The purpose of this study is to analyze and predict the financial market returns and various indexes based on deep learning CNN neural network algorithm in image processing technology. This study uses the time series method, using the convolution pooling process in CNN to effectively capture the local correlation characteristics of financial market data, then extract the important information hidden in the time series data, draw the trend curve of this information, and combine the features through image processing technology, finally realize the prediction of the financial market time series income index. The results show that in the deep learning algorithm of this study, the highest actual value of stock price after image processing is 3374, and the highest error value is 5.176%, which is nearly 20% less than other algorithms. When N1 = 1600, 3032 sliding windows are obtained, and the Euclidean norm of this point is 0.1586. The conclusion is that the deep learning algorithm of this study is effective and accurate for the prediction of financial market series. Image processing and data analysis technology provide effective methods and make important contributions to the research of financial field.

The major studied in this dissertation is the non-destructive testing method of silkworm cocoon's quality, based on the digital image processing and photoelectricity technology. Through the images collection and the data analysis, procession and calculation of the tested silkworm cocoons with the non-destructive testing technology, internet applications automatically reckon all items of the classification indexes. Finally we can conclude the classification result and the purchase price of the silkworm cocoons. According to the domestic classification standard of the silkworm cocoons, the author investigates various testing methods of silkworm cocoons which are used or have been explored at present, and devices a non-destructive testing scheme of the silkworm cocoons based on the digital image processing and photoelectricity technology. They are dissertated about the project design of the experiment. The precisions of all the implements are demonstrated. I establish Manifold mathematic models, compare them with each other and analyze the precision with technology of databank to get the best mathematic model to figure out the weight of the dried silkworm cocoon shells. The classification methods of all the complementary items are designed well and truly. The testing method has less error and reaches an advanced level of the present domestic non-destructive testing technology of the silkworm cocoons.

A posture estimation algorithm based on image processing technology involves analyzing images or video frames to determine the position and orientation of human bodies or specific body parts. This process typically begins with detecting key points or landmarks on the body, such as joints or anatomical features. This paper presents a novel approach for athletes' action recognition and posture estimation using image processing technology, specifically focusing on the Contour Mapping Multivariant Recognition and Estimation (CMMRE) algorithm. The primary objective is to develop a robust system capable of accurately identifying human actions and estimating postures from visual data, with potential applications in sports analysis, surveillance, and human-computer interaction. The CMMRE algorithm employs advanced techniques for contour mapping, feature extraction, and prediction, leveraging deep learning methodologies to analyze and interpret visual information effectively. Through a series of experiments and simulations, the algorithm's performance is evaluated, showcasing its ability to achieve high accuracy in action recognition tasks across various scenarios. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement. The algorithm achieved an average accuracy of 95% in recognizing walking and running actions, and 80% in identifying jumping actions. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement.

This paper gives a point to detect transmission line broken stocks defect problems by using image processing and recognition technology, which has obtained the good effect in tests. By comparing with a set of template, the wire broken stocks defects can be extracted from the complex background identify. The method can be applied to power line inspection by helicopters and unmanned aerial vehicle (uav) in a variety of ways.

This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.

In today’s agricultural development, field weeds can be said to be a strong enemy of agricultural production. In the production of traditional agriculture, herbicides and other related pesticides are mainly used to ensure the normal production of crops, which will cost a lot of manpower, material resources, financial resources and a large number of herbicides, and also cause serious environmental pollution. The development prospect of modern agriculture is precision agriculture. How to realize the sustainable development of agriculture, reduce pollution and achieve the goal of precision weeding in the current era is the difficult problem of agricultural development. Therefore, in this paper, the corn seedling stage as a model, the artificial intelligence technology and other electronic technology are integrated into the agricultural management system, and the weeds in the corn seedling stage are identified by using the weed digital image processing and recognition technology, so as to accurately spray herbicides, so as to achieve the purpose of improving agricultural productivity. Through the analysis, it is found that the method proposed in this paper has important practical significance in the development of digital image processing and recognition technology of maize seedling Weeds Based on artificial intelligence technology.

With the developments of artificial intelligence and image processing technology, the study of multiple vision tasks has become a hot topic in the field of computer vision, and the key to implementing vision tasks lies in the feature extraction of images. To meet the demands of modern life for multiple vision tasks and promote the deployment of artificial intelligence on edge devices, we propose an image feature extraction method applied to multiple vision tasks, which promotes the deployment of artificial intelligence on edge devices. Firstly, the function of the secondary task in multi-task learning is combined with a feature migration strategy to build a feature migration model for multiple vision tasks. Then, a generic feature extraction model based on feature fusion and feature migration is established under the guidance of image classification. The experimental results show that the proposed feature migration model with image classification as the secondary task can improve the mAP (mean average precision) of the target detection by 5.5%, and the feature migration model with target detection as the secondary task can improve the average accuracy of the image classification by 1%. The proposed generic feature extraction model can effectively implement the image classification and target detection tasks.

Image processing analysis and research based on game animation design

In the field of basketball, the existing training concept is based on the simulated observation and personal experience of the coaches, and it is subject to subjective judgment of inadequacies. Using image technology to train athletes is mainly to help coaches make decisions and improve the strength of athletes through the identification and recognition of athletes' states and transfer characteristics. The purpose of this paper is to study the basketball pose motion feature extraction algorithm based on image processing technology. This paper firstly builds the basketball posture model, introduces the feature extraction and selection in basketball posture, analyzes the application of image processing algorithm in basketball posture movement feature extraction, and mainly uses filtering algorithm to denoise the moving image. The algorithm in this paper mainly selects the normalized data processing algorithm to perform data induction processing on the feature data extracted in this paper. Experiments show that after image processing technology, the recognition rate of motion features by BP neural network is the highest, and the average recognition rate reaches 96%, which can effectively recognize the motion features of basketball posture.

Nowadays, the rapid development of sports science and technology has brought about the vigorous development of the sports industry, which is worth studying for a long time in the future. This article aims to use metallurgical sintering technology to prepare high‐performance iron‐based powder materials and apply them in the traditional sports industry to produce new sports equipment. This article proposes a metallurgical sintering method, that is, the use of a process of powdered iron‐containing materials through a series of operations to finally obtain a new agglomerate and to obtain the required new physical and mechanical properties of materials or products. A new type of material is made and used in the traditional sports industry to meet the high‐quality needs of sports equipment in the current era. According to survey statistics, my country’s sports industry has been developing rapidly in the past five years. It has increased from 1,901.13 billion yuan in 2016 to 2,869.35 billion yuan in 2020. Sports investment and financing events have also dropped from 313 to 105, but the entire sports industry’s investment is getting bigger and bigger. The number of employees in the national sports industry has also exceeded the 2 million mark. In the future, the sports industry is also an industry with great potential for development.

The digital image processing and recognition technology is applied to the detection of the print quality of screen printing, which is designed to implement the application system of some functions in screen printing, such as the acquisition of multiple printing images, the feature extraction and the matching to the quality control processing. In the RG Chromaticity space, the template matching method is used to design the relevant algorithms and realize fast real-time detection of some problems related to the image dislocation, leakage India, infiltration excess and uneven quality color. Having been tested and run, the method appears good quality monitoring results and economic benefits.

The sports industry is considered the industry with the most vitality, participation, and penetration in the 21st century. From the perspective of practice on a global scale, the demand for the sports market has gradually developed from low-level simplification to high-level multi-polarity, and sports consumption has also shifted from single sports goods to sports services and entertainment sports. The sport industry has become modern society containing emerging industries with large business opportunities. This article mainly uses literature, expert interviews, data collation, logical analysis, and other methods to conduct a comparatively systematic study on the development of the new form of the “Internet+” sports industry. In the study, by comparing the commodity types, organizational forms, and management of the traditional sports industry in terms of management and other aspects, the connotation of the new format of the “Internet+” sports industry is introduced in detail, which is divided into four industries: network broadcasting, smart software and hardware, vertical e-commerce, and e-sports. The research in this article shows that, after accounting, the total scale (total output) of the national sports industry in 2019 was 2,948.3 billion yuan, with a year-on-year added value of 1,124.8 billion yuan. In terms of growth, total output increased by 10.9% over 2018 and 11.6% year-on-year. Among them, sports activities integrated with the Internet have reached 58%. Finally, it will help the transformation and upgrading of Internet+sports and improve its own development level. Returning to the real economy and serving the real economy have become a consensus for the future development of Internet finance.

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.

This paper proposed a new method based on image processing and pattern recognition technology to recognize the pattern of woven fabric. The method disposed the information of both the top and bottom fabric images. The result of analyzing the regularity of the intensity variation of the horizontal and vertical direction of image can be used to determine the interlacing position of yarn sections. According to encoding the color information of fabric, the types of cross points can be obtained. After getting the color information of fabric on the warp and weft directions, the color pattern could be determined, and it can be used to correct the errors of classification of cross-point classification. Finally a fabric model can be built which contains types of interlacing points and color pattern.