Color recognition system with augmented reality concept and finger interaction: Case study for color blind aid system

2012 ◽  
Author(s):  
Alfa Sheffildi Manaf ◽  
Riri Fitri Sari
Keyword(s):  
Augmented Reality ◽  
Recognition System ◽  
Color Recognition ◽  
Color Blind

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

Augmented Reality to increase efficiency of MEP construction: a case study

2021 ◽  
Author(s):  
Patrick Dallasega ◽  
Felix Schulze ◽  
Andrea Revolti ◽  
Martin Martinelli
Keyword(s):  
Augmented Reality ◽  
Increase Efficiency

Case Study: Enhancing Human Reliability With Artificial Intelligence and Augmented Reality Tools for Nuclear Maintenance

2018 ◽  
Author(s):  
Geoffrey Momin ◽  
Raj Panchal ◽  
Daniel Liu ◽  
Sharman Perera
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Human Error ◽  
Nuclear Industry ◽  
Industrial Accidents ◽  
Worker Productivity ◽  
Energy Agency ◽  
Computer Mediated ◽  
Critical Feedback

Human error accounts for about 60% of the annual power loss due to maintenance incidents in the fossil power industry. The International Atomic Energy Agency reports that 80\% of industrial accidents in the nuclear industry can be attributed to human error and 20\% to equipment failure. The Personal Augmented Reality Reference System (PARRS) is a suite of computer-mediated reality applications that looks to minimize human error by digitizing manual procedures and providing real-time monitoring of hazards present in an environment. Our mission is to be able to provide critical feedback to inform personnel in real-time and protect them from avoidable hazards. PARRS aims to minimize human error and increase worker productivity by bringing innovation to safety and procedural compliance by leveraging technologies such as augmented reality, LiDAR, computer machine learning and particulate mapping using remote systems.

A Hero to Be Remembered:EbonyMagazine, Critical Memory and the “Real Meaning” of the King Holiday

2016 ◽  
Vol 52 (2) ◽  
pp. 503-527
Author(s):  
E. JAMES WEST
Keyword(s):  
Ronald Reagan ◽  
Important Case ◽  
Living History ◽  
Black America ◽  
Senior Editor ◽  
History Of ◽  
Color Blind ◽  
Martin Luther King

This article explores the role ofEbonymagazine as a key staging ground for competing political and ideological debates over Martin Luther King Jr's legacy, and the struggle for a national holiday in his name. In doing so, it provides an important case study into the contestations between what Houston Baker has described as “black critical memory” and “black conservative nostalgia.” In response to attempts by Ronald Reagan and other politicians to reimagine King as an advocate for color-blind conservatism,Ebony’s senior editor, Lerone Bennett Jr., sought to situate King's legacy within a radical “living history” of black America. However, the magazine's broader coverage of the King holiday movement betrayed underlying tensions within its discussion of King's legacy, and fed into the magazine's role as an inadvertent frame for color-blind ideologies during the 1980s.

Real-Time Occlusion Between Real and Digital Objects in Augmented Reality

2018 ◽  
Author(s):  
Kevin Lesniak ◽  
Conrad S. Tucker
Keyword(s):  
Augmented Reality ◽  
Coordinate System ◽  
Real Time ◽  
Real World ◽  
Virtual Object ◽  
Virtual Objects ◽  
Common Coordinate System ◽  
Realistic Representation ◽  
Lead Users

The method presented in this work reduces the frequency of virtual objects incorrectly occluding real-world objects in Augmented Reality (AR) applications. Current AR rendering methods cannot properly represent occlusion between real and virtual objects because the objects are not represented in a common coordinate system. These occlusion errors can lead users to have an incorrect perception of the environment around them when using an AR application, namely not knowing a real-world object is present due to a virtual object incorrectly occluding it and incorrect perception of depth or distance by the user due to incorrect occlusions. The authors of this paper present a method that brings both real-world and virtual objects into a common coordinate system so that distant virtual objects do not obscure nearby real-world objects in an AR application. This method captures and processes RGB-D data in real-time, allowing the method to be used in a variety of environments and scenarios. A case study shows the effectiveness and usability of the proposed method to correctly occlude real-world and virtual objects and provide a more realistic representation of the combined real and virtual environments in an AR application. The results of the case study show that the proposed method can detect at least 20 real-world objects with potential to be incorrectly occluded while processing and fixing occlusion errors at least 5 times per second.

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