Artificial Intelligence Vision Research Articles

Artificial Intelligence Vision Based on Computer Digital Technology in 3D Image Colour Processing

Based on computer digital technology, the paper first uses the method of non-equal interval quantization to quantize the pixels in the 3D image, and introduces the artificial intelligence vision technology HSV colour space to construct the feature vector formula after non-equal interval quantization to calculate the difference between the two images the final distance between. Experiments have shown that the algorithm has better robustness to changes in light intensity while maintaining the lower number of quantized bins in the Tahoun algorithm. At the same time, the algorithm can effectively reduce 3D colours, with better visual effects, and better than the original. The interference of images with large differences on the search results improves the recall rate of the image library used in the experiment.

A Study on the Automated Payment System for Artificial Intelligence-Based Product Recognition in the Age of Contactless Services

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

Quality of Hand Radiograph Collimation Determined by Artificial Intelligence Algorithm Correlates with Radiograph Quality Scores Assigned by Radiologists

Providing direct feedback to technologists has become challenging for radiologists due to geographic separation and other reasons. As such, there is a need for automated solutions to solve quality issues in radiography. We evaluated the feasibility of using a computer vision artificial intelligence (AI) algorithm to classify hand radiographs into quality categories in order to automate quality assurance processes in radiology. A bounding box was placed over the hand on 300 hand radiographs. These inputs were employed to train the computational neural network (CNN) to automatically detect hand boundaries. The trained CNN detector was used to place bounding boxes over the hands on an additional 100 radiographs, independently of the training or validation sets. A computer algorithm processed each output image to calculate unused air spaces. The same 100 images were classified by two musculoskeletal radiologists into four quality categories. The correlation between the AI-calculated unused space metric and radiologist-assigned quality scores was determined using the Spearman correlation coefficient. The kappa statistic was used to calculate the inter-reader agreement. The best negative correlation between the AI-assigned metric and the radiologists’ assigned quality scores was achieved using the calculation of the unused space at the top of the image. The Spearman correlation coefficients were −0.7 and −0.6 for the two radiologists. The kappa correlation coefficient for interobserver agreement between the two radiologists was 0.6. Automatic calculation of the percentage of unused space or indirect collimation at the top of hand radiographs correlates moderately well with radiographic collimation quality.

Learning to See in Extremely Low-Light Environments with Small Data

Recent advances in deep learning have shown exciting promise in various artificial intelligence vision tasks, such as image classification, image noise reduction, object detection, semantic segmentation, and more. The restoration of the image captured in an extremely dark environment is one of the subtasks in computer vision. Some of the latest progress in this field depends on sophisticated algorithms and massive image pairs taken in low-light and normal-light conditions. However, it is difficult to capture pictures of the same size and the same location under two different light level environments. We propose a method named NL2LL to collect the underexposure images and the corresponding normal exposure images by adjusting camera settings in the “normal” level of light during the daytime. The normal light of the daytime provides better conditions for taking high-quality image pairs quickly and accurately. Additionally, we describe the regularized denoising autoencoder is effective for restoring a low-light image. Due to high-quality training data, the proposed restoration algorithm achieves superior results for images taken in an extremely low-light environment (about 100× underexposure). Our algorithm surpasses most contrasted methods solely relying on a small amount of training data, 20 image pairs. The experiment also shows the model adapts to different brightness environments.

Design Lessons From AI’s Two Grand Goals: Human Emulation and Useful Applications

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool; 2) simulated teammate or teleoperated device; 3) autonomous system or supervisory control; and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety.

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist.

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection

Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection

Classification Algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment

This paper describes the design and development of a Classification Algorithms Framework (CAF) using the JetBrains MPS domain-specific languages (DSLs) development environment. It is increasingly recognized that the systems of the future will contain some form of adaptivity therefore making them intelligent systems as opposed to the static systems of the past. These intelligent systems can be extremely complex and difficult to maintain. Descriptions at higher-level of abstraction (system-level) have long been identified by industry and academia to reduce complexity. This research presents a Framework of Classification Algorithms at system-level that enables quick experimentation with several different algorithms from Naive Bayes to Logistic Regression. It has been developed as a tool to address the requirements of British Telecom's (BT's) data-science team. The tool has been presented at BT and JetBrains MPS and feedback has been collected and evaluated. Beyond the reduction in complexity through the system-level description, the most prominent advantage of this research is its potential applicability to many application contexts. It has been designed to be applicable for intelligent applications in several domains from business analytics, eLearning to eHealth, etc. Its wide applicability will contribute to enabling the larger vision of Artificial Intelligence (AI) adoption in context.

Image intelligent automatic processing analysis based on artificial intelligence vision generator

The image intelligent processing analysis technology uses a computer to imitate and execute some intellectual functions of the human brain, and realizes an image processing system with artificial intelligence, that is, an image processing analysis technology is an understanding of an image. The degree of intelligent automated analysis and processing is low, many operations need to be done manually, causing human error, inaccurate detection, and time-consuming and laborious. Deep learning method can extract features step by step in the original image from the bottom to the top. Therefore, based on feature analysis technology, this paper uses the deep learning method to intelligently and automatically analyse the visual image. This method only needs to send the image into the system, and then the manual analysis is not needed, and the analysis result of the final image can be obtained. The process is completely intelligent and automatically processed. First, improve the deep learning model and use massive image data to choose and optimize parameters. Results indicate that our method not only automatically derives the semantic information of the image, but also accurately understands the image accurately and improve the work efficiency.

PPE Compliance Detection using Artificial Intelligence in Learning Factories

This project demonstrates the application of Artificial Intelligence (AI) and machine vision for the identification of Personal Protective Equipment (PPE), particularly safety glasses in zones of the Learning Factory, where safety risks exist. The objective is to design and implement an automated system for ensuring the safety of personnel when they are in the vicinity of machinery that presents potential risks to the eyes. Microsoft Azure Custom Vision AI and Intelligent AI Services, in conjunction with low-cost vision devices with lightweight onboard AI capability, provide a platform for a deep learning neural network model using publicly available images under the Creative Commons License. A combination of cloud-based and on-premises AI is used in this proof of concept system to provide a real-time vision-based safety system capable of detecting and recording potential safety breaches, promoting compliance, and ultimately preventing accidents before they happen. This system can be used to initiate different control actions in the event of safety violations and can detect multiple forms of protective wear. The flexibility of the system offers multiple benefits to learning factories and manufacturing organizations such as improved user safety, reduced insurance costs, and better detection and recording of safety violations. The hybrid AI architecture approach allows for flexibility in training and deployment based on the capability of local computing resources.

Artificial Intelligence: Achievements and Postponed Risks

The article considers the problem of the development of artificial intelligence, its ambivalent impact on society and humanity. There are the description and definition of artificial intelligence, the problem field was defined. We conducted a study of the manifest and latent functions of artificial intelligence, which can provoke postponed risks. We considered the vision of artificial intelligence as a complex socio-biotechnological hybrid and a qualitatively new mechanism of social control. The achievements and risk-based role of artificial intelligence in modern complex society was estimated. Particular attention is paid to the nature of knowledge about the phenomenon of artificial intelligence, acquiring an interdisciplinary character. Some deferred risks related to the functioning of artificial intelligence are indicated. The factors contributing to the creation of a socio-biotechnological hybrid in the form of artificial intelligence are considered in the context of the riskological turn. The risks of a new type of irrational rationality are considered, the cause of which, according to the author, is the introduction of IT and artificial intelligence, the consequences of rationality are given. The problem of the risk of deintimization, depriving a person of opportunities and the right to personal information is noted. That problem may entail new manifestations of alienation in the form of the dehumanization of social and natural realities. due to the introduction in many programs of artificial intelligence. The article deals with the problem of the pluralization of knowledge about artificial intelligence, the use of performance mechanisms that produce fakes, absolutizing positive or negative consequences of the functioning of artificial intelligence. The article summarizes the state of the risks of artificial intelligence at the moment. Recommendations for further interdisciplinary study of artificial intelligence are presented. The article substantiates the importance of the implementation of the risk assessment of the emerging programs based on artificial intelligence.

Machine learning applications in critical care

The use of machine learning (ML) applications in the intensive care units (ICUs) has surged over the last two decades. This is the result of the digital transformation that many health-care organizations have implemented. Data that are generated in the process of intensive care have more volume, velocity, and value than data generated in any other general hospital's department. This characteristic of ICUs makes them attractive environments for developing models that require rich dataset. ML has been used to develop clinical decision support system (CDSS) that could make informative decisions without requiring prior in-depth knowledge about the roots of the disease or common characteristics of the patients. The adoption of ML-based CDSS in ICUs is continuously increasing as ML algorithms achieve high levels of accuracy in descriptive, diagnostic, predictive, and prescriptive decisions. This article reviews some of the applications of ML in ICUs. This article will show examples of how ML was used for outcome predictions, such as predicting mortality and readmission. Examples in this article also include using ML for diagnostic and image recognition purposes. This review will discuss the use of ML for monitoring ICU patients, whether monitoring their physical safety with artificial intelligence vision detection algorithms, monitoring their continuous bedside measurements, or monitoring the administration and dosage of their medications. All these examples show that ML-based CDSS are on the path for a journey full of innovative and creative solutions that will increase the quality, efficiency, and effectiveness of critical care.

Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Augmented Reality (AR) is crucial for immersive Human–Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry. Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

Target recognition system of dynamic scene based on artificial intelligence vision

Target recognition system of dynamic scene based on artificial intelligence vision

THE POPULAR APPEAL OF APOCALYPTIC AI

AbstractThe belief that computers will soon become transcendently intelligent and that human beings will “upload” their minds into machines has become ubiquitous in public discussions of robotics and artificial intelligence in Western cultures. Such beliefs are the result of pervasive Judaeo‐Christian apocalyptic beliefs, and they have rapidly spread through modern pop and technological culture, including such varied and influential sources asRolling Stone, theIEEE Spectrum, and official United States government reports. They have gained sufficient credibility to enable the construction of Singularity University in California. While different approaches are possible (and, indeed, are common in Japan and possibly elsewhere), this particular vision of artificial intelligence and robotics has gained ground in the West through the influence of figures such as Hans Moravec and Ray Kurzweil. Because pop‐science books help frame public discussion of new sciences and technologies for individuals, corporations, and governments alike, the integration of religious and technoscientific claims made by their authors should be clear and open for public and scientific debate. As we move forward into an increasingly robotic future, we should do so aware of the ways in which a group's religious environment can help set the tone for public acceptance and use of robotic technologies.

COMPARATIVE ANALYSIS OF LEONARDO AI, MIDJOURNEY, AND DALL-E: AI'S PERSPECTIVE ON FUTURE CITIES

This paper aims to deepen our understanding of the intricate role that Artificial Intelligence (AI) plays in the ever- changing landscape of urban design and planning, building on a seminal study conducted a year ago that utilized the Stable Diffusion 1.5 algorithm to investigate "AI's Vision of Future Cities". This Study, version 2.0, provides a comprehensive comparison and evaluation of three cutting-edge AI text-to-image generation algorithms: LeonardoAI, Midjourney and Dall-E with the same technique of the previous study. Using curated series of prompts that integrate a variety of crucial urban design themes, such as smart cities, sustainability, eco-design, biomimicry, and topology optimization, the study provides a nuanced evaluation of the unique capabilities, strengths, and limitations inherent in each of these advanced AI tools. In the findings, although LeonardoAI is proficient at providing a balanced thematic representation, it tends to produce less realistic images. Midjourney v5.2 is distinguished by its ability to generate visually striking yet exaggerated depictions of urban elements with variety. Dall-E 3 is the most usable and practical tool for generating clear and coherent urban elements, despite its logical limitations. The study concludes by emphasizing the context-dependence of the effective and meaningful application of AI tools in the multifaceted and complex domain of urban design. In addition, it argues that these AI tools are most effective when used in conjunction with conventional design and planning techniques. This research not only extends and elaborates on the previous study s findings, but it also explicit the visuality in rapid change of the AI image synthesis for the groundwork of future research avenues.

A low cost artificial intelligence vision system for piece part recognition and orientation

SUMMARY Traditional feature extraction vision techniques are well proven. However, in certain circumstances they suffer from some severe disadvantages such as processing time, image complexity, internal features and lighting levels. This paper describes the use of a hybrid system using artificial intelligence techniques in combination with traditional feature extraction techniques which overcome some disadvantages. This hybrid system is able to locate and orientate parts with complex internal features and to provide an indication of quality.

Geometrical Modeling from Multiple Stereo Views

The components of the Sheffield Artificial Intelligence Vision Research Unit (AIVRU) three-dimensional (3D) vision sys tem, which currently supports model-based object recognition and location, are described. Its potential for robotics applica tions is demonstrated by its guidance of a Universal Machine Intelligence robot arm in a pick-and-place task. The system comprises (1) the recovery of a sparse depth map using edge- based, passive stereo triangulation; (2) the grouping, descrip tion, and segmentation of edge segments to recover a 3D representation of the scene geometry in terms of straight lines and circular arcs; (3) the statistical combination of 3D de scriptions for object model creation from multiple stereo views and the propagation of constraints for within-view re finement ; and (4) the matching of 3D wireframe object models to 3D scene descriptions in order to recover an initial estimate of their position and orientation.

The Perception of Motion: Michotte Revisited

This paper extends the range of artificial-intelligence vision programs to the domain of moving objects, using the work of Michotte as a source of data for a computer program which purports to 'see' the action types he examined. Given the thesis that internal descriptions of a nonlinguistic type provide the basis for a constructive, knowledge-based theory of perception, the properties of a symbol-manipulating system capable of supporting this are explored as a challenge to the Michotte notion of "direct perception without interpretation".