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Head-mounted tablets (HMTs), a type of augmented reality (AR) wearable device that is worn on the head like glasses, have gained vast attention in the manufacturing industry as they enable workers to receive hands-free support. For emerging technologies, it could be useful to predict their acceptance among potential users. Hence, various researchers have utilized the technology acceptance model (TAM) to forecast such acceptance, in the past decade also including HMTs and other AR smart glasses. In this research, an exploratory model is developed to investigate which factors allow to predict a future acceptance of HMTs for training new employees on the shop floor. After collecting 46 survey responses and applying a partial least squares structural equation modeling (PLS-SEM) approach, the findings indicate that the protection of personal data and satisfaction with the technology significantly influence the usage of HMTs among new employees. Furthermore, a significant effect was found for experience and ease of use.

In this paper we present an improved color-based planar fiducial marker system. Our framework provides precise and robust full 3D pose estimation of markers with superior accuracy when compared with many fiducial systems in the literature, while color information encoding enables using over 65 000 distinct markers. Unlike most color-based fiducial frameworks, which requires prior classification training and color calibration, ours can perform reliably under illumination changes, requiring but a rough white balance adjustment. Our methodology provides good detection performance even under poor illumination conditions which typically compromise other marker identification techniques, thus avoiding the evaluation of otherwise falsely identified markers. Several experiments are presented and carefully analyzed, in order to validate our system and demonstrate the significant improvement in estimation accuracy of both position and orientation over traditional techniques.

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR.

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.

This study aims to contribute to the electronics education through the use of Augmented Reality (AR) technology, and thus, limit the dependency on a physical environment and the equipment required for the experiments performed in electronics education. In this regard, an Augmented Reality-based mobile application (ARElectronicLab) has been designed to provide a technology–reality blended experience of electronic circuits in real physical life. This AR-based mobile application has been used to create simulations of diode clipper circuit and inverting operational amplifier circuit. The mobile application operates with a marker in real life and enables monitoring of 3D simulations of electronic components through a touch screen. Hence, the application offers a real-like experience and brings an innovative and enriching perspective into the electronics education.

Spatial thinking is the ability to mentally visualize and manipulate 3D objects, identify patterns, and describe relationships among objects in a setting. These skills are highly predictive of success in engineering, architecture, graphic design, computer science, and other fields of education. In our education system, at the school level, teachers focus on something other than the development of the skills mentioned above. Which is passed on to the next cadre and even reflected in the professional lives of students. Most research on spatial thinking ability develops at the undergraduate or primary levels. However, there is a need to work on adolescents in middle school who are at the stage of learning and manipulating abstract concepts. In this study, a spatial thinking learning application based on AR technology is developed and evaluated using Unity 3D with AR foundation libraries. AR technology’s augmented and immersive characteristics helped adolescents (12–13 years) visualize objects in three dimensions and make inferences about them. One group pre and posttest pre-experimental research design was followed. Statistically significant results of pre- and post-surveys and graphs of in-app calculations provide evidence of learning. This experience allowed students to actively practice spatial constructs, enhance their spatial reasoning skills, and boost their self-efficacy while using these skills.

The following topics are under this section:

• Healing with Technology
• Smart Hospital: An Instrument of Care
• Artificial Intelligence (AI) in Precision Cancer Diagnostics: Myth or Magic?
• Transforming healthcare with data and Artificial Intelligence (AI)

This paper presents a dynamical solution of camera registration problem for on-road navigation applications via a 3D-2D parameterized model matching algorithm. The traditional camera'fs three dimensional (3D) position and pose estimation algorithms have always employed fixed and known-structure models as well as the depth information to obtain the 3D-2D correlations, which is however unavailable for on-road navigation applications since there are no fixed models in the general road scene. With the constraints of road structure and on-road navigation features, this paper presents a 2D digital road-based road shape modeling algorithm. Dynamically generated multi-lane road shape models are used to match real road scenes to estimate the camera 3D position and pose data. Our algorithms have successfully simplified the 3D-2D correlation problem to the 2D-2D road model matching on the projective image. The algorithms proposed in this paper are validated with the experimental results of real road tests under different conditions and types of road.

Camera pose estimation from video images is a fundamental problem in machine vision and Augmented Reality (AR) systems. Most developed solutions are either linear for both n points and n lines, or iterative depending on nonlinear optimization of some geometric constraints. In this paper, we first survey several existing methods and compare their performances in an AR context. Then, we present a new linear algorithm which is based on square fiducials localization technique to give a closed-form solution to the pose estimation problem, free of any initialization. We also propose an hybrid technique which combines an iterative method, in fact the orthogonal iteration (OI) algorithm, with our own closed form solution. An evaluation of the methods has shown that this hybrid pose estimation technique is accurate and robust. Numerical experiments from real data are given comparing the performances of our hybrid method with several iterative techniques, and demonstrating the efficiency of our approach.

In a total hip arthroplasty surgery, correctly implanting the artificial acetabulum and the femoral head is essential for a successful treatment. An augmented reality (AR) navigation framework is proposed in this paper to provide accurate surgical guidance in a total hip arthroplasty procedure. The AR framework consists of three parts: (1) preoperative surgical planning to generate virtual information for AR; (2) computer vision-based tracking for the real-time localization of both acetabular cup positioner and bony landmarks during surgery; (3) registration of a virtual object onto a real-world operative field to properly overlay the preoperative surgical planning data onto a three-dimensional (3D)-printed pelvis model. The cost-effective framework is designed with our clinical partner based on real surgical conditions. The bony landmarks are automatically detected and used for the registration between virtual and real objects. The AR performance is evaluated with a pelvis model, and it presents mean errors of 2.2mm and 0.8^∘ in position and orientation, respectively, between real and virtual spaces. These small errors are within the tolerance of positive surgical outcomes.

Neuroendoscopic surgery is a minimally invasive surgical technique commonly used to remove a tumor through the patient’s mouth or nose, which requires the surgeon to avoid important neural and vascular structures. Augmented reality-based surgical navigation can provide surgeons with more information and visual aids, clarifying the structure and details of the surgical area. It requires real-time registration of intraoperative endoscopic video with pre-operative CT models. However, 3D reconstruction and camera tracking from nasal endoscopic video are challenging due to the narrow nasal cavity and the lack of texture. Besides, the nasal endoscopic datasets that can be used for deep learning-based depth estimation are scarce and hard to elaborate. To this end, a style-augmented module (SAM) is proposed in this study to minimize discrepancies between different endoscopic datasets. An unsupervised approach was trained to generate depth maps and camera motion paths, which were used to reconstruct the nasal scene’s 3D model. The obtained reconstruction was aligned with a pre-operative CT model for augmented reality-based surgical navigation. The results showed that the proposed SAM improved the generalization of the depth estimation model on nasal endoscopy data. The proposed approach to combining augmented reality technology with surgical navigation is considered instrumental in furnishing surgeons with richer information on the nasal endoscopic surgical scene.

The aim of this research is develop an effective virtual prototyping system for product development using augmented reality technology. Before a virtual environment is put into work for design and development, some way of quantifying errors or uncertainties in the computer model is needed so that a robust and reliable system can be achieved. This paper presents the calibration, registration, and preparation of an augmented reality environment with 3D tracking and dynamic simulation technologies for studying dynamic systems, such as parts orientation devices. With such virtual prototyping techniques, engineers can run high-fidelity simulation to test new materials, components, and systems before investing valuable resources in construction of physical prototypes.

In this paper, we present a vision-based localization system using mobile augmented reality (MAR) and mobile audio augmented reality (MAAR) techniques, applicable to both humans and humanoid robots navigation in indoor environments. In the first stage, we propose a system that recognizes the location of a user from the image sequence of an indoor environment using its onboard camera. The location information is added to the user's view in the form of 3D objects and audio sounds with location information and navigation instruction content via augmented reality (AR). The location is recognized by using the prior knowledge about the layout of the environment and the location of the AR markers. The image sequence can be obtained using a smart phone's camera and the marker detection, 3D object placement and audio augmentation will be performed by the phone's operating processor and graphical/audio modules. Using this system will majorly reduce the hardware complexity of such navigation systems, as it replaces a system consisting of a mobile PC, wireless camera, head-mounted displays (HMD) and a remote PC with a smart phone with camera. In the second stage, the same algorithm is employed as a novel vision-based autonomous humanoid robot localization and navigation approach. The proposed technique is implemented on a humanoid robot NAO and improves the robot's navigation and localization performance previously done using an extended Kalman filter (EKF) by presenting location-based information to the robot through different AR markers placed in the robot environment.

We evaluate the ability of locally present participants to localize an avatar head’s gaze direction rapidly in hosted telepresence. We performed a controlled user study to test two potential solutions to indicate a remote user’s gaze. We analyze the influence of the user’s distance to the avatar and display technique on localization accuracy. Furthermore, we examine the effect of the avatar head’s rotation around the pitch and yaw axes on the localization accuracy. Our experimental results suggest that all these factors have a significant effect on the localization accuracy with different extent. The results also verified that people had a better perceptive and localizing ability when interacting with a 3D AR-displayed avatar. In addition, participants preferred to interact with a 3D avatar displayed using a simulated AR technique instead of with a 2D avatar displayed in a tablet because of its high realism. Our findings motivate the need for 3D avatar display in the further design of telepresence systems.

Today, with the rapid development of science and technology, the value of education is more and more valued by people, but the research and development of quality education in film and television are still relatively traditional. The objective is to explore the application and effect of the combination of artificial intelligence and deep learning algorithm in the quality education of youth film and television. In view of the current problems in the quality education of film and television, this work innovatively introduces the augmented reality (AR) technology, and applies artificial intelligence and AR technology to the quality education of film and television for young people. At the same time, a deep learning algorithm is introduced to build a youth film and television quality education system based on artificial intelligence combined with deep learning, and further empirical analysis of the system is carried out in the form of a questionnaire survey. The questionnaire survey shows that, from the perspective of various dimensions of learning attitude, the two groups of learners had significant differences in emotional experience and self-cognition ($P<0.05$), and more than 90% of the teenagers thought the system resource interface was beautiful; from the perspective of the perceived usefulness of the system, 68.24% of the teenagers believe that the system is easy to operate and useful, and as many as 82.82% of the teenagers believe that the system improves their interest in learning about quality education courses in film and television. Therefore, it is found that the constructed system can improve the learning interest of young people in the quality education course of film and television and make their learning attitude more positive, providing experimental reference for the latter research in the field of youth education.

Multiple emerging technologies both threaten grocers and offer them attractive opportunities to enhance their value propositions, improve processes, reduce costs, and therefore generate competitive advantages. Among the variety of technological innovations and considering the scarcity of resources, it is unclear which technologies to focus on and where to implement them in the value chain. To develop the most probable technology forecast that addresses the application of emerging technologies in the grocery value chain within the current decade, we conduct a two-stage Delphi study. Our results suggest a high relevance of almost all technologies. The panel is only skeptical about three specific projections. As a consequence, grocers are advised to build up knowledge regarding the application of these technologies in the most promising areas of their value chain.

In contemporary Tourism industry, DMOs are necessary to adopt and offer innovative experience in order to attract contemporary tourists. Gamification, combined with Augmented Reality and all the relevant to this technology innovations are examined in this paper. Efforts should rely on the demands and needs of generation Z tourists as up and coming generation. However, there are specific implications such as the use of augmented reality smart glasses, incentives and personal data protection that occur. This paper contributes in understanding the new Generation called “Z”, under the light of tourism and the effective use of augmented reality for tourism purposes, combined in one travel-product experience.

This study aimed to investigate and provide insight into the drivers and motivations behind users’ intentions for using car dashcams. The study utilized a purposive sampling technique with a sample size of 225 and obtained responses through a structured questionnaire. Partial least squares structural equation modeling (PLS-SEM) was performed to verify the proposed hypotheses. The findings demonstrated that the perceived quality of the system, the quality of the information, and the quality of the interaction positively influenced the perceived ease of use and usefulness of using car dashcams. Attitude and behavioral intention were affected by perceived ease of use and usefulness, while perceived usefulness had no influence on intention to use car dashcams. While hedonic motivation and price value impacted behavioral intention, social intention did not affect it. Attitude was found to fully mediate the path between perceived usefulness and behavioral intention. The outcomes of the study incorporate a considerable amount of knowledge into the adoption behavior of telematics literature by integrating prevalent technology adoption models into a unified model: The information system success model (ISS), technology acceptance model (TAM), and unified theory of acceptance and use of technology 2 (UTAUT2). The findings provide valuable insights for marketers, the dash cam industry, and policymakers to enhance their understanding of the critical factors affecting the adoption of car dashcams, which can assist in customizing their marketing campaigns and policies to encourage the usage of dashcams, leading to improved road safety and safe driving behavior.

Local features are widely used for content-based image retrieval and augmented reality applications. Typically, feature descriptors are calculated from the gradients of a canonical patch around a repeatable keypoint in the image. In this paper, we propose a temporally coherent keypoint detector and design efficient interframe predictive coding techniques for canonical patches and keypoint locations.

In the proposed system, we strive to transmit each patch with as few bits as possible by simply modifying a previously transmitted patch. This enables server-based mobile augmented reality where a continuous stream of salient information, sufficient for image-based retrieval and localization, can be sent over a wireless link at a low bit-rate. Experimental results show that our technique achieves a similar image matching performance at 1/15 of the bit-rate when compared to detecting keypoints independently frame-by-frame and allows performing streaming mobile augmented reality at low bit-rates of about 20–50 kbps, practical for today's wireless links.