With the rapid development of artificial intelligence technology, the network image recognition technology of machine learning is intelligent and production and life in computer vision system technology in China have gradually achieved significant success. On this basis, the computer network image recognition system is constructed by machine learning with image features such as cross-verification, fitting, accuracy, feature selection, and dimensionality reduction, and the results show that the machine learning algorithm improves the stability and multi-domain application of computer image recognition through cross-verification and deviation of decision tree units. Recognition technologies such as feature vector extraction, edge information, and texture features of machine learning algorithms improve the accuracy of image recognition.

With the development of the times, sports competitions are sought after and favored by more and more people, and the judgment of athletes in the competition is becoming more and more strict and standardized. The further development of Internet technology and information technology has provided great convenience for the effective recognition of athletes’ movements in sports competitions. This article aims to study the background of information technology, through the use of sports visual image technology to design and construct an action recognition system, in order to efficiently and accurately identify and judge the various movements of sports competitions, thereby effectively reducing various manpower and the cost of material resources, while improving the accuracy of the competition results. In the experiment, this article invites 100 volunteers to participate in the system’s behavioral test recognition. The volunteers’ simple exercises such as jumping, raising hands, kicking, turning and squatting are tested, and the result is the exercise designed in this article. The recognition system is able to recognize five sets of actions in the experiment, among which jumping is 97.66%, kicking is 98.13%, squatting is 97.62%, raising hand is 95.24%, and turning is 96.43%. Research shows that the motion recognition system based on sports visual image technology designed and constructed in this paper has high motion recognition accuracy and can recognize athletes’ movements scientifically and effectively.

In collaborative software development, programmers create branches for simultaneous program editing, and merge branches to integrate edits. When branches divergently edit the same text, the edits conflict and cannot get co-applied. Tools were built to automatically merge software branches, to detect conflicts, and to resolve conflicts along the way. However, there is no third-party benchmark or metric to comprehensively evaluate or compare those tools.This paper presents ConflictBench, our novel benchmark to evaluate software merge tools. ConflictBench consists of 180 merging scenarios extracted from 180 open-source Java projects. For each scenario, we sampled a conflicting chunk (i.e., conflict) reported by git-merge. Because git-merge sometimes wrongly reports conflicts, with our manual inspection, we labeled 136 of the 180 chunks as true conflicts, and 44 chunks as false conflicts. To facilitate tool evaluation, we also defined a systematic method of manual analysis toanalyze all program versions involved in each merging scenario, and to summarize the root causes as well as developers’ resolution strategies. We further defined three novel metrics to evaluate merge tools. By applying five state-of-the-art tools to ConflictBench, we observed that ConflictBench is effective to characterize different tools. It helps reveal limitations of existing tools and sheds light on future research.

Software Configuration Management (SCM) aims to provide a controlling mechanism for the evolution of software artifacts created during software development process. Controlling software artifacts evolution requires many activities to be carried out such as, construction and creation of versions, computation of mappings and differences between versions, merging (i.e. combining of two or more versions) and so on. Traditional SCM systems are file-based SCM systems, which are not adequate for performing software configuration management activities. File-based SCM systems consider software artifacts as a set of text files, while today’s software development is model-driven and models are the main artifacts produced in the early phases of software development process. New challenges of model mappings, differencing, merging, and conflict detection arise when applying file-based solution to model-driven software. The goal of this paper is to develop a configuration management solution for model merging and conflict resolution that overcomes the challenges faced by traditional SCM systems for model-based development. We represent models at finegrained level as graph structures, which is an intermediate representation based on graph theory. Our approach follows a 3-way model merge process, where a base and its derived versions are used for comparison. To differentiate between conflicted and non-conflicted cases, we have defined different merge cases, and established a merge policy based on merge cases. Merge cases are used along with the comparison result in order to perform conflict resolution and merge operation. We performed a controlled experiment using open source eclipse modeling framework and compare our approach with an open source tool Eclipse Modeling Framework (EMF) Compare. The results proved the accuracy and efficiency of our proposed approach.

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

AbstractThe emerging optoelectronic neuromorphic devices are widely concerned due to their capability to integrate the functions of signal sensing, memory, and processing. Although significant advancements have been made in the study of individual optoelectronic synaptic devices, the development of hardware‐level image recognition systems based on photo‐synapse arrays remains a challenge. In this study, a crosstalk‐free, easy‐to‐integrate, and scalable 8 × 8 crossbar array for optical image sensing and storage is demonstrated using vertical two‐terminal ZnO photo‐synapses with the self‐denoising function. By designing peripheral circuits, a complete hardware‐level artificial visual system is constructed that successfully implements the real‐time pattern recognition tasks for 8 × 8 pixel images. The excellent performance of the photo‐synapse array shows its remarkable ability in highly efficient optic neuromorphic computing. Additionally, an in‐sensor reservoir computing (RC) system is constructed for image recognition of handwritten digits. The system achieves a high classification accuracy of 95.1%.

With the rapid development of China’s national economy, the number of cars on domestic expressways and in cities has greatly increased. Cities have more and more facilities, such as roads and parking lots, while traffic control and safety management requirements are increasing. In Japan and overseas, Intelligent Transportation System (ITS) has become the main direction of current traffic management development and image recognition based on image recognition technology. As one of the core technologies of computer intelligent transportation system, computer technology is an important part of intelligent transportation system. The wide application of this technology will make a great contribution to the automation of traffic management in China.

The construction of image recognition system is inseparable from the development of computer network technology and artificial intelligence. Although the previous large-scale integrated circuit technology has made amazing achievements, it still cannot directly perceive the sound, image, text and other information. With artificial intelligence and modern network technology to open up new achievements in this research field, it is particularly important to carry out the research of image recognition system. The bronze culture and art of the Chinese Bronze Age are the crystallization of the wisdom of the ancient Chinese labouring people and a precious heritage our ancestors left us. How to preserve these precious cultural heritages with the means and methods of modern science and technology is a necessary process to further understand the time-honored characteristics of the Chinese nation. This paper will carry out the research from the Angle of modern artificial intelligence network technology integrating art, and strive to depict and preserve the colorful bronze culture systematically and comprehensively. This paper tries to construct a set of bronze cultural image recognition and management system, so that most users can realize the appreciation and management of ancient culture in a modern way through the intervention of artificial intelligence and network technology.

With the development of science and technology, especially the development of computer technology, visual technology on the base of digital image processing technology provides the means of observation for the medical objective. On the other hand, the computer aided design and computer aided manufacturing which are widely used in mechanical processing industry provide the effective method for complex shape precision machined. This image recognition software is designed mainly aimed at conformal lead mould CAD/CAM system, and it is the part of the system. The result of the final implementation is that target area edge data is outlined in a single pixel extracted from the form and is stored in the form of text style file.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.