Robotic imaging, machine learning and augmented reality for computer assisted interventions

2021 ◽  
Author(s):  
Nassir Navab
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Computer Assisted

scholarly journals Machine Learning for Communications

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Video Streaming ◽  
Communication Networks ◽  
Data Analytics ◽  
Data Traffic ◽  
Traffic Demand ◽  
Performance Requirements

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

scholarly journals VISUALIZATION OF E-COMMERCE DATA USING AUGMENTED REALITY

2020 ◽  
Vol 5 (19) ◽  
pp. 104-122
Author(s):  
Azzan Amin ◽  
Haslina Arshad ◽  
Ummul Hanan Mohamad
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Data Visualization ◽  
Data Science ◽  
Moving Average ◽  
Open Data ◽  
Visual Presentation ◽  
Depth Dimension ◽  
2D Data ◽  
Visualization Application

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).

scholarly journals An Exploration of Augmented Reality in Computer Assisted Orthopaedic Surgery

10.29007/72d4 ◽  
2018 ◽  
Author(s):  
He Liu ◽  
Edouard Auvinet ◽  
Joshua Giles ◽  
Ferdinando Rodriguez Y Baena
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Hole Drilling ◽  
Depth Information ◽  
Computer Assisted ◽  
Pilot Hole ◽  
Surgical Workflow ◽  
Processing Depth ◽  
Computer Assisted Orthopaedic Surgery ◽  
Preoperative Plan

Computer Aided Surgery (CAS) is helpful, but it clutters an already overcrowded operating theatre, and tends to disrupt the workflow of conventional surgery. In order to provide seamless computer assistance with improved immersion and a more natural surgical workflow, we propose an augmented-reality based navigation system for CAS. Here, we choose to focus on the proximal femoral anatomy, which we register to a plan by processing depth information of the surgical site captured by a commercial depth camera. Intra-operative three-dimensional surgical guidance is then provided to the surgeon through a commercial augmented reality headset, to drill a pilot hole in the femoral head, so that the user can perform the operation without additional physical guides. The user can interact intuitively with the system by simple gestures and voice commands, resulting in a more natural workflow. To assess the surgical accuracy of the proposed setup, 30 experiments of pilot hole drilling were performed on femur phantoms. The position and the orientation of the drilled guide holes were measured and compared with the preoperative plan, and the mean errors were within 2mm and 2°, results which are in line with commercial computer assisted orthopedic systems today.

scholarly journals Use of Augmented Reality to Create an iOS App with Watson Studio

2021 ◽  
Vol 4 ◽  
pp. 98-100
Author(s):  
Semen Gorokhovskyi ◽  
Yelyzaveta Pyrohova
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Augmented Reality ◽  
New Technologies ◽  
Higher Education Institutions ◽  
Rapid Development ◽  
Easy Access ◽  
Smart Devices ◽  
The Real ◽  
The World

With the rapid development of applications for mobile platforms, developers from around the world already understand the need to impress with new technologies and the creation of such applications, with which the consumer will plunge into the world of virtual or augmented reality. Some of the world’s most popular mobile operating systems, Android and iOS, already have some well-known tools to make it easier to work with the machine learning industry and augmented reality technology. However, it cannot be said that their use has already reached its peak, as these technologies are at the stage of active study and development. Every year the demand for mobile application developers increases, and therefore more questions arise as to how and from which side it is better to approach immersion in augmented reality and machine learning. From a tourist point of view, there are already many applications that, with the help of these technologies, will provide more information simply by pointing the camera at a specific object.Augmented Reality (AR) is a technology that allows you to see the real environment right in front of us with a digital complement superimposed on it. Thanks to Ivan Sutherland’s first display, created in 1968 under the name «Sword of Damocles», paved the way for the development of AR, which is still used today.Augmented reality can be divided into two forms: based on location and based on vision. Location-based reality provides a digital picture to the user when moving through a physical area thanks to a GPS-enabled device. With a story or information, you can learn more details about a particular location. If you use AR based on vision, certain user actions will only be performed when the camera is aimed at the target object.Thanks to advances in technology that are happening every day, easy access to smart devices can be seen as the main engine of AR technology. As the smartphone market continues to grow, consumers have the opportunity to use their devices to interact with all types of digital information. The experience of using a smartphone to combine the real and digital world is becoming more common. The success of AR applications in the last decade has been due to the proliferation and use of smartphones that have the capabilities needed to work with the application itself. If companies want to remain competitive in their field, it is advisable to consider work that will be related to AR.However, analyzing the market, one can see that there are no such applications for future entrants to higher education institutions. This means that anyone can bring a camera to the university building and learn important information. The UniApp application based on the existing Swift and Watson Studio technologies was developed to simplify obtaining information on higher education institutions.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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