Multimedia Computing Systems and Virtual Reality

Cover
Half Title
Title Page
Copyright Page
Contents
Editors
Contributors
1. Use of Virtual Reality in Exposure Therapy and Other Psychological Treatment Methods
1.1 Introduction
1.2 Psychology
1.3 The workings and quality of the simulation
1.3.1 Intervention Approach
1.3.2 Efficacy
1.4 Benefits
1.4.1 Lower Refusal and Drop-Out Rates
1.4.2 More control to Therapist
1.4.3 Data collection and Its Uses
1.4.4 Additional Benefits
1.5 VR in the use of specific disorders
1.5.1 Phobias
1.5.2 Panic Disorders
1.5.3 Anxiety Disorders
1.5.4 Post-Traumatic Stress Disorder
1.5.5 Obsessive-Compulsive Disorders
1.5.6 Pain Management
1.5.7 Addiction
1.5.8 More Complex Disorders
1.6 Methods to administer VRET in therapy
1.6.1 Components of VR Clinical System
1.7 Issues and resolution
1.8 Discussion
References
2. Role of Swarm Intelligence and Neural Network in Intelligent Traffic Management
2.1 Introduction
2.2 Related Work
2.3 Swarm Intelligence, IoT and Neural Network in ITM
2.4 Swarm Intelligence (SI)-Based Methods
2.5 Element of IoT Technology
2.6 IoT Architecture
2.7 Deep Learning
2.7.1 Types of Deep Learning Methods
2.8 Advantages of an ITM
2.9 Key Applications of ITM
2.10 Intelligent Transport System (ITM) Functionalities
2.11 Challenges Besides Transport in Metropolitan Centers
2.12 Conclusions
References
3. Image Systems and Visualizations
3.1 Introduction
3.1.1 Computer Vision Is an Ill-posed Inverse Problem
3.1.2 Mid-Level Vision
3.1.3 High-Level Vision
3.2 Fundamentals of Computer Vision
3.2.1 Pre-Processing
3.2.1.1 Image Enhancement
3.2.1.2 Image Restoration
3.2.1.3 Image Compression
3.2.1.4 Image Filtering
3.2.2 Background Subtraction
3.2.2.1 First Difference Method
3.2.2.2 Gaussian Mixture Method
3.2.2.3 Collaborative Mask
3.2.3 Image Segmentation
3.2.3.1 Thresholding
3.2.3.2 Region Based
3.2.3.3 Edge Based
3.2.3.4 Topology Based
3.2.3.5 K-means Clustering
3.2.4 Object Detection
3.2.4.1 R-CNN
3.2.4.2 Fast R-CNN
3.2.4.3 Faster R-CNN
3.2.4.4 YOLO
3.2.4.5 SSD
3.2.4.6 R-FCN
3.2.5 Feature Extraction
3.3 Vision in Single and Multiple Images
3.3.1 Stereopsis
3.3.2 Photometric Stereo
3.3.3 Shape from Shading
3.3.4 Structure from Motion
3.4 Applications of Computer Vision
3.4.1 Personal Photo
3.4.2 Surveillance and Security
3.4.3 Optical Character Recognition (OCR)
3.4.4 Face Recognition
3.4.5 Virtual Reality
3.4.6 Natural Language Processing (NLP)
3.4.7 Assistive Technologies
3.4.8 Biometrics and Forensics
3.4.9 Astronomy and Cosmic Study
3.4.10 Machine Inspection
3.4.11 Games
3.4.12 Panorama Stitching
3.4.13 Robotics
3.4.14 Image Fusion
3.4.15 Navigation
3.4.16 3D Reconstruction
3.4.17 Traffic Management and Monitoring
3.4.18 Defence and Military Applications
3.4.19 Template Matching
3.4.20 Object Recognition
3.4.21 Motion Tracking
3.4.22 Content-Based Image Retrieval
3.4.23 Weather Forecasting
3.5 Problem Implementation: Unattended Object Detection
3.5.1 What Is an Unattended Object?
3.5.2 Input Video
3.5.3 Background Subtraction
3.5.4 Static Foreground Region Detection
3.5.5 Owner Identification
3.5.6 Thresholding
3.5.7 Social Context Mapping
3.5.8 Unattended Object Detection
3.5.9 Object Identification
3.5.10 Alert Triggering
3.5.11 Special Cases: Occlusion and Multiple Events
3.5.12 Implementation and Result
3.6 Conclusion
References
4. Virtual Reality in Social Media Marketing: The New Age Potential
4.1 Introduction
4.1.1 Virtual Reality (VR) and Marketing
4.1.2 The Allure of Virtual Reality (VR) to Digital Marketers
4.2 The Changing Era of Technology
4.2.1 The Evolution of Virtual Reality VR
4.2.2 Extended Reality - The Current Technological Landscape
4.2.2.1 Input Devices for VR
4.2.2.2 Output Devices for VR
4.2.3 Virtual Reality and the COVID-19 Pandemic
4.3 The Evolution of Marketing Strategies
4.3.1 The Digital Era of Social Media Marketing
4.3.2 Bridging Gaps in Marketing through Virtual Reality
4.4 Applications of Extended Reality - Companies Being Innovators
4.4.1 Industries and Their Adoption of Extended Reality
4.4.2 The Use of Extended Reality in Marketing
4.4.2.1 Extended Reality in the Consumer Electronics Industry
4.4.2.2 Extended Reality in the Beauty and Fashion Industries
4.4.2.3 Extended Reality in the Retail and E-commerce Industries
4.4.2.4 Extended Reality in the Real Estate Industry
4.4.2.5 Extended Reality in the Hospitality Industry
4.4.2.6 Extended Reality in the Travel and Tourism Industry
4.4.3 Immersive Virtual Reality and Social Networks
4.5 The Future of Virtual Reality and Social Media Marketing
4.6 Conclusion
References
5. An Efficient Deep Learning Framework for Multimedia Big Data Analytics
5.1 Introduction
5.2 Deep Learning
5.2.1 Basic Architecture
5.2.2 Learning Process
5.2.2.1 Threshold Function
5.2.2.2 Sigmoid Function
5.2.2.3 Hyperbolic Tangent Function
5.2.2.4 Rectifier Function
5.3 Deep Learning Frameworks
5.3.1 TensorFlow
5.3.2 Keras
5.3.3 Theano
5.3.4 Torch
5.3.5 CNTK (Computational Network Tool Kit)
5.3.6 Deeplearning4j
5.3.7 Caffe
5.4 Classification
5.4.1 Convolutional Neural Network (CNN)
5.4.1.1 Convolutional Layer
5.4.1.2 Activation Function
5.4.1.3 ReLU
5.4.1.4 Pooling Layer
5.4.1.5 Max Pooling
5.4.1.6 Min Pooling
5.4.1.7 Fully Connected Layer
5.4.2 Alex Net Model
5.4.2.1 First Level
5.4.2.2 Second Level
5.4.2.3 Third Level
5.4.3 VGG-16 Model
5.5 Object Detection
5.5.1 Fast R-CNN
5.5.2 Faster R-CNN
5.5.3 You Only Look Once (YOLO)
5.6 Single Shot Detector
5.7 Applications of Multimedia Analytics Based on Deep Learning Techniques
5.7.1 Big Data Analytics
5.7.2 Healthcare Monitoring System
5.7.3 Social Media
5.8 Conclusions
References
6. An Optimal System on Data Challenge with Distributed Data Management on Cloud, Fog and Edge Computing
6.1 Introduction
6.2 Infrastructure and integration with provisioning and management systems
6.2.1 Service-Enabling the Infrastructure
6.2.2 The Goal of Automation and Orchestrate Methods
6.2.3 Integration of Configuration Management and Improvisation Engines
6.2.4 Codifying Deployment Policies
6.2.5 Inter-cloud Architecture
6.2.6 Addressing Topological Dependencies
6.3 Five challenges of a traditional data warehouse
6.3.1 Inflexible Structure
6.3.2 Complex Architecture
6.3.3 Slow Performance
6.3.4 Outdated Technology
6.3.5 Lack of Governance
6.4 The Importance of adaptation
6.4.1 Flexibility
6.4.2 Consolidate IT Infrastructure
6.4.3 Scalability
6.5 Edge taxonomy and framework
6.5.1 Architectural Trends at the Service Provider Edge
6.5.2 Safety and Management of Distributed Equipment
6.5.3 Edge Computing Use Cases
6.6 The fog computing data platform
6.6.1 Managing Resources in Microdata Centres
6.6.2 Distributed Data Storage Mechanisms
6.6.3 Data Distribution
6.6.4 Data Dissemination
6.6.5 Privacy Preservation in Fog Computing
6.7 Conclusion
References
7. Anomaly Detection in Real-Time Videos Using Match Subspace System and Deep Belief Networks
7.1 Introduction
7.2 Categories of anomaly
7.2.1 Point Anomaly
7.2.2 Contextual Anomaly
7.2.3 Collective Anomaly
7.2.4 Techniques in Anomaly Detection
7.3 Digital video tampering detection
7.4 Spatio-temporal video-volume conformation inside video
7.5 Rotation-invariant attribute modeling motion coherence (RIMOC)
7.6 Learning deep depictions of arrival and motion
7.7 Real-time troop comportment detection in video
7.8 Locality sensitive hashing filters (LSHF)
7.9 Frame deletion detection method
7.10 Challenges
7.11 Related works
7.12 Proposed model
7.12.1 Stage 1
7.12.1.1 Deep representation of mid-level features
7.12.1.2 Multiscale motion mapping with deep belief networks
7.12.2 Stage 2: Identifying High-Level Features with a Fusion Model Framework
7.12.3 Stage 3: Anomaly Detection
7.13 Anomaly detection with one class support vector machines
7.14 Results
7.15 Conclusions & future scope
References
8. Innovation in Multimedia Using IoT Systems
8.1 Introduction
8.1.1 Objective of the Study
8.2 IoT-Enabled Multimedia Frameworks
8.2.1 Compression in Wireless Network Sensing
8.3 IoT-Based Multimedia Applications
8.3.1 Internet of Multimedia Things for Real-Time Streaming
8.3.2 Internet of Multimedia Things for Smart Healthcare
8.3.3 Internet of Multimedia Things for Agriculture
8.3.4 Internet of Multimedia Things for Smart Home and Energy Management
8.3.5 Internet of Multimedia Things for a Smart City
8.4 Proposed Framework for IoT Multimedia Service Delivery
8.5 Challenges and Limitations
8.5.1 Internet of Medical Things Security and Sustainability
8.5.2 IoMT Network Connection and Performance Matrix
8.5.3 Image Processing and Video Surveillance in IoT
8.5.4 Outdated Software Versions and Hardware Devices
8.5.5 Autonomous Systems for Data Management (Monitoring and Control)
8.6 Open Research Issues
8.6.1 IoMT Privacy and Security
8.6.2 Lack of Standardization and Platform Interoperability
8.6.3 IoMT and Quality of Experience (QoE)
8.6.4 IoMT and Device-to-Device Communication
8.7 Conclusion
Author contributions
References
9. Virtual Reality and Augmented Reality for Education
9.1 Introduction of Virtual Reality
9.2 Three Hundred and Sixty-Degree Images and Videos
9.2.1 Three-Dimensional Objects/Techniques
9.2.2 2D/3D, Volumetric Display and Projection Technology
9.2.3 Experimental Volumetric Display in 2D and 3D Using an Image
9.2.4 Types of Volumetric Display
9.2.5 Technical Challenges
9.3 User Experience of Video with VR Devices
9.3.1 Evaluation of User Method
9.4 Classification of Virtual Reality Systems
9.4.1 Immersive Analytical and Visualization
9.4.2 Immersive Demonstrations
9.4.3 Immersive Analytics in VR
9.4.4 Types of Immersion VR
9.5 3D Visualization
9.6 Uses of Virtual Reality
9.6.1 Advantages of Virtual Reality
9.6.2 Disadvantages of Virtual Reality
9.7 VR in Education
9.8 Teacher Preparation and New Teacher Training
9.8.1 Digital Classroom
9.8.2 Increased Learning Possibilities
9.9 Virtual Tools for Learning in the Real World
9.9.1 Effective Online Classroom
9.9.2 Interactive Virtual Field Trips
9.9.3 Art Education
9.9.4 Tutoring
9.9.5 Challenges
9.10 Creating Additional Content
9.10.1 VR Headsets Are Available
9.10.2 The Sickness of the Internet
9.11 Augmented Reality in Education
9.12 How AR Works in Education
9.13 How Do We Use Augmented Reality in the Education Field?
9.14 AR Development in Education: Lead the Way of Innovation or Are Left Behind
9.15 Augmented Reality Apps for Education
9.15.1 Making Learning Interesting to More Students
9.15.2 Visualizing Complex Concepts
9.15.3 Learning Becomes Globally Accessible
9.15.4 Refining Professional Skills
9.15.5 Reducing Hardware and Equipment Costs
9.15.6 Outlook for Augmented Reality in Schools and Beyond
9.15.7 Building an AR Application in Education
a A Swift and Effective Learning System
b Easy Access to Learning Materials Anytime, Anywhere
c Immersive Practical Learning
d Engage Students and Increase Their Interest
e Wrapping Up
9.16 Augmented Reality Technology in Education
9.17 Open Research Issues and Future of VR and AR
9.18 Conclusion
References
10. Fog- or Edge-Based Multimedia Data Computing and Storage Policies
10.1 Introduction
10.2 Innovation Services
10.2.1 Principle of Computing
10.2.2 Meaning of Edge and Fog Computing
10.2.3 Scientific Edge and Cloud Computing
10.3 Features of Fog and Edge Computing
10.4 Concepts of Fog Computing
10.5 Models/Architectures
10.5.1 A Generic Fog Computing Architecture
10.5.2 Framework Fog and Edge Computing Model
10.5.3 A Fog Computing Architecture
10.5.4 Fog Computing Tree Model
10.6 Comparison between Cloud Computing and Fog Computing
10.7 Promise of Cloud and Fog Computing
10.7.1 Fundamental Concept in Edge, Cloud and Fog Computing
10.7.2 Taxonomy of Edge Computing
10.8 Contradiction in Cloud Computing and Fog Computing
10.8.1 Network of Fog
10.8.2 Quality Service (QoS)
10.8.3 Interface and Programming Model
10.8.4 Computer Offload
10.8.5 Delivery and Resource Management
10.8.6 Confidentiality and Security
10.9 Legal Dimensions of Cloud Computing and Fog Computing
10.10 Conclusion
References
11. Role of Virtual Reality and Multimedia Computing in Industrial Automation System
11.1 Introduction
11.2 Literature Survey
11.3 Virtual Reality and Multimedia Computing
11.3.1 How Does VR Technology Work?
11.3.2 Key Principle of VR
11.3.3 Issues with VR Technology
11.3.4 Relationship among Multimedia Computing and VR Technology
11.4 Key Technologies of VR
11.4.1 Sensor Interface and Motion Tracking
11.4.2 Visualization Components and Devices
11.4.3 3D Sounds
11.4.4 Graphics and Animation
11.5 Challenges in Manual Industrial System
11.5.1 Training for the Manufacturing Sector
11.5.2 Workplace Accidents and Injuries
11.5.3 Latest Techniques/Innovations Must Be Established in the Industry
11.6 Industrial Automation System
11.6.1 Benefits of Industrial Automation
11.6.2 Key Challenges in Industrial Automation
11.7 VR Applications in Industrial Automation System
11.7.1 Smart Manufacturing
11.7.2 Asset Management and Tracking
11.7.3 Visualization and Simulation
11.7.4 Workers' Safety
11.7.5 Automation of Documentation Work
11.8 Conclusions
References
12. Virtual Reality-Based Education
12.1 Introduction
12.2 Benefits of virtual reality in education
12.2.1 Better Place Sense
12.2.2 Scaling the Experience of Learning
12.2.3 Learn by Doing
12.2.4 Emotional Reaction
12.2.5 Develop Creativity
12.2.6 Visual Learning
12.2.6.1 VR for Visual Learners
12.2.6.2 VR for Auditory Learners
12.3 New technology is available to users
12.3.1 Educational Categories of VR
12.3.1.1 Virtual Fields Trips
12.3.2 High-Tech Training
12.3.3 Internships
12.3.4 Group Learning
12.3.4.1 Distance Learning
12.3.4.2 Challenges in Design for VR Training Experiences
12.4 Five main characteristics of a VR experience
12.5 Development of a new role for VR educators
12.5.1 Knowledge of Immersive Experiences
12.5.2 Making VR Accessible
12.6 Open new horizons with VR education - Future
References
13. Concentrated Gaze Base Interaction for Decision Making Using Human-Machine Interface
13.1 Introduction
13.2 Proposed method for gaze estimation
13.2.1 Calibration
13.2.2 Estimation of Gaze-Tracking Accuracy
13.3 Eye-gaze estimation algorithm
13.4 Experimental system and results
13.4.1 Perception of Human Intentions
13.4.2 Intention Inference
13.4.3 Visual Object Intention
13.5 Conclusion
References
Index