Deep Learning for Multimedia Processing Applications. Volume Two, Signal Processing and Pattern Recognition

Deep Learning for Multimedia Processing Applications Volume Two, Signal Processing and Pattern Recognition Volume Two, Signal Processing and Pattern Recognition /

Detalles Bibliográficos
Otros Autores: Bhatti, Uzair Aslam, 1986- editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Multimedia systems.
Deep learning (Machine learning)
Digital video.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009826132606719
Tabla de Contenidos:
  • Intro
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Contents
  • Contributors
  • Chapter 1 A Review on Comparative Study of Image-Denoising in Medical Imaging
  • 1.1 Introduction
  • 1.1.1 Evaluation of Image-Denoising Techniques
  • 1.1.2 Image-Denoising Techniques
  • 1.1.3 Applications
  • 1.1.4 Comparison
  • 1.2 Conclusion
  • References
  • Chapter 2 Remote-Sensing Image Classification: A Comprehensive Review and Applications
  • 2.1 Introduction
  • 2.1.1 Need for This Survey
  • 2.1.2 Need for Remote-Sensing Image Classification
  • 2.1.3 Significance of Remote-Sensing Image Classification
  • 2.1.4 Research Gap for Deep Learning-Based Remote-Sensing Image Classification
  • 2.2 Deep Learning Architectures for Remote-Sensing Image Classification
  • 2.2.1 Convolutional Neural Networks (CNNs)
  • 2.2.2 Fully Convolutional Networks (FCNs)
  • 2.2.3 U-Net
  • 2.2.4 SegNet
  • 2.2.5 DeepLab
  • 2.2.6 Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) Networks
  • 2.2.7 Autoencoders
  • 2.2.8 Generative Adversarial Networks (GANs)
  • 2.2.9 Capsule Networks (CapsNets)
  • 2.2.10 Attention-Based Mechanisms
  • 2.2.11 Graph Convolutional Networks (GCNs)
  • 2.2.12 Siamese Networks
  • 2.2.13 3D Convolutional Neural Networks (3D-CNNs)
  • 2.3 Differences in Deep Learning Architectures for Remote-Sensing Image Classification
  • 2.3.1 Architecture
  • 2.3.2 Input Data
  • 2.3.3 Training Strategy
  • 2.3.4 Transfer Learning
  • 2.3.5 Optimization
  • 2.3.6 Interpretability
  • 2.4 Remote-Sensing Data Sources and Characteristics
  • 2.4.1 Satellites
  • 2.4.2 UAVs
  • 2.4.3 Multispectral Sensors
  • 2.4.4 Hyperspectral Sensors
  • 2.4.5 Synthetic Aperture Radar (SAR)
  • 2.4.6 Spatial Resolution
  • 2.4.7 Spectral Resolution
  • 2.4.8 Temporal Resolution
  • 2.4.9 Radiometric Resolution
  • 2.5 Application of Deep Learning in Remote Sensing.
  • 2.5.1 Land Cover Classification
  • 2.5.2 Vegetation Monitoring
  • 2.5.3 Urban Land-Use Classification
  • 2.5.4 HSI Remote Sensing
  • 2.6 Challenges for Deep Learning Methods for RS Image Processing
  • 2.6.1 High Spatial and Spectral Variability
  • 2.6.2 Limited Annotated Data
  • 2.6.3 Class Imbalance
  • 2.6.4 Intra-Class Variability
  • 2.6.5 Spectral Mixing and Shadow Effects
  • 2.6.6 Sensor Noise and Atmospheric Interference
  • 2.6.7 Computational Complexity
  • 2.6.8 Adaptability and Generalization
  • 2.7 Limitations
  • 2.7.1 Dependence on Image Quality
  • 2.7.2 Temporal Variability
  • 2.7.3 Generalization and Transferability
  • 2.7.4 Scalability
  • 2.7.5 Interpretability
  • 2.7.6 Labeling Challenges
  • 2.7.7 Privacy Concerns
  • 2.7.8 Legal and Policy Constraints
  • 2.8 Conclusion
  • 2.8.1 Future Work
  • Funding
  • References
  • Chapter 3 Deep Learning Framework for Face Detection and Recognition for Dark Faces Using VGG19 with Variant of Histogram Equalization
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Material and Techniques
  • 3.3.1 Convolutional Neural Network
  • 3.3.2 Histogram Equalization
  • 3.3.3 Data Set
  • 3.3.4 Proposed Framework
  • 3.4 Experiment and Results
  • 3.4.1 Evaluation Parameter
  • 3.4.2 Experimental Setup
  • 3.4.3 Results and Analysis
  • 3.5 Conclusion
  • References
  • Chapter 4 A 3D Method for Combining Geometric Verification and Volume Reconstruction in a Photo Tourism System
  • 4.1 Introduction
  • 4.2 Literature Review
  • 4.3 Method
  • 4.3.1 Processing
  • 4.3.2 Feature Extraction
  • 4.3.3 Feature Matching
  • 4.3.4 Geometric Verification
  • 4.3.5 Volumetric Reconstruction
  • 4.3.6 Triangulation
  • 4.3.7 Bundle Adjustment
  • 4.4 Experiments
  • 4.4.1 Data Set Description
  • 4.5 Results and Analysis
  • 4.6 Conclusion
  • Acknowledgments
  • References.
  • Chapter 5 Deep Learning Algorithms and Architectures for Multimodal Data Analysis
  • 5.1 Introduction to Multimodal Data Analysis
  • 5.2 Overview of Deep Learning Algorithms and Architectures
  • 5.2.1 Pre-Processing of the Multimodal Data
  • 5.2.2 The Training Process of Deep Learning Models on Multimodal Data
  • 5.2.3 Deep Learning Methods and Blockchain Technology Consortium
  • 5.3 Conclusion and Future Directions
  • Abbreviations
  • References
  • Chapter 6 Deep Learning Algorithms: Clustering and Classifications for Multimedia Data
  • 6.1 Introduction
  • 6.1.1 Deep Learning and Its Applications in Multimedia Data Analysis
  • 6.1.2 Classification Algorithms in Deep Learning Can Be Broadly Classified into the Following Categories
  • 6.1.3 Deep Learning for Clustering Multimedia Data
  • 6.1.4 Types of Clustering
  • 6.1.5 Classification of Clustering Algorithms in Deep Learning
  • 6.1.6 List of More Comprehensive Deep Learning Clustering Algorithms
  • 6.2 Deep Clustering Algorithm Challenges and the Multimedia Data
  • 6.2.1 Convolutional Autoencoders for Clustering Images
  • 6.2.2 Deep Convolutional Embedded Clustering for Clustering Images
  • 6.2.3 Multimodal Deep Embedded Clustering for Clustering Multimodal Data
  • 6.2.4 Case Studies - The Effectiveness of Deep Learning for Clustering Multimodal Data
  • 6.3 Deep Learning for Classification of Multimedia Data
  • 6.3.1 Incorporating Multimodal Data for Improving Classification Performance
  • 6.4 Blockchain Technology and Deep Learning Algorithms in the Context of Multimodal Data
  • 6.4.1 Cross-Border Blockchain Technology and Deep Learning Methods
  • 6.4.2 Key Attributes of Cross-Border Blockchain Technology
  • 6.4.3 Cross-Border and Deep Learning Multimodal Blockchain Technology
  • 6.4.4 Future Trends and Applications of the Blockchain and Deep Learning
  • 6.5 Conclusion
  • Abbreviations.
  • References
  • Chapter 7 A Non-Reference Low-Light Image Enhancement Approach Using Deep Convolutional Neural Networks
  • 7.1 Introduction
  • 7.2 Literature Review
  • 7.3 Material and Techniques
  • 7.3.1 Retinex Theory
  • 7.3.2 Decomposition Network
  • 7.3.3 Optimizing the Network
  • 7.4 Experiment and Results
  • 7.4.1 Experimental Design
  • 7.4.2 Subjective Evaluation
  • 7.4.3 Objective Evaluation
  • 7.4.4 Generalization Ability
  • 7.5 Conclusion
  • References
  • Chapter 8 Human Pose Analysis and Gesture Recognition: Methods and Applications
  • 8.1 Introduction
  • 8.2 Literature Review
  • 8.2.1 Bodily Attached Sensors Based Methods
  • 8.2.2 Computer Vision-Based Recognition Systems
  • 8.2.3 Pose and Gesture Recognition Using Multiple Sensors
  • 8.2.4 Data Fusion in a Multisensory Environment
  • 8.2.5 Pose and Gesture Data Set
  • 8.3 Conclusion
  • References
  • Chapter 9 Human Action Recognition Using ConvLSTM with Adversarial Noise and Compressive-Sensing-Based Dimensionality Reduction, Concise and Informative
  • 9.1 Introduction
  • 9.2 Background
  • 9.3 Proposed Model
  • 9.3.1 Data Layer
  • 9.3.2 Compressive Sensing
  • 9.3.3 Feature Learning
  • 9.3.4 Sequential Learning
  • 9.3.5 Softmax
  • 9.3.6 Output Layer
  • 9.4 Results and Discussion
  • 9.4.1 SVM Classifier
  • 9.4.2 ConvLSTM Classifier
  • 9.4.3 ConvLSTM with GANs
  • 9.4.4 Overall Results
  • 9.5 Conclusions
  • Supplementary Materials
  • Acknowledgments
  • References
  • Chapter 10 Application of Machine Learning to Urban Ecology
  • 10.1 Introduction
  • 10.1.1 Brief Background of Urban Ecology
  • 10.1.2 The Importance of Machine Learning in Urban Ecology
  • 10.1.3 Objectives of the Chapter
  • 10.2 Introduction to Machine Learning
  • 10.2.1 Types of Machine Learning Techniques
  • 10.2.2 How Machine Learning Can Benefit Urban Ecology
  • 10.3 Overview of Urban Ecological Data Sources.
  • 10.3.1 Preprocessing and Data Fusion Techniques
  • 10.4 Applications of Machine Learning in Urban Ecosystem Services
  • 10.4.1 Urban Green Space Identification and Monitoring
  • 10.4.2 Biodiversity Assessment and Conservation
  • 10.4.3 Urban Heat Island Detection and Mitigation
  • 10.4.4 Air Quality Monitoring and Prediction
  • 10.4.5 Flood Risk Assessment and Management
  • 10.5 Applications of Machine Learning in Urban Landscape Planning and Design
  • 10.5.1 Landscape Connectivity and Fragmentation Analysis
  • 10.5.2 Green Infrastructure Planning
  • 10.5.3 Urban Greening and Rewilding Strategies
  • 10.5.4 Urban Form Optimization for Ecological Resilience
  • 10.5.5 Evaluation of Landscape Design Alternatives
  • 10.6 Machine Learning for Socio-Ecological Systems in Urban Environments
  • 10.6.1 Analyzing Human-Nature Interactions
  • 10.6.2 Environmental Justice and Equitable Access to Green Spaces
  • 10.6.3 Public Engagement and Decision-Making Support
  • 10.6.4 Community-Based Ecological Monitoring and Management
  • 10.7 Challenges and Future Directions
  • 10.7.1 Data Quality and Availability
  • 10.7.2 Interpreting and Validating Machine Learning Models
  • 10.7.3 Integrating Cross-Disciplinary Knowledge
  • 10.7.4 Ethical Considerations
  • 10.7.5 Climate Change and Urban Ecology
  • 10.8 Conclusion
  • References
  • Chapter 11 Application of Machine Learning in Urban Land Use
  • 11.1 Introduction
  • 11.1.1 Briefly Introduce the Concept of Machine Learning and Urban Land Use
  • 11.1.2 Explain the Significance of Integrating Machine Learning in Urban Planning and Management
  • 11.2 Background: Understanding Urban Land Use and Machine Learning
  • 11.2.1 Discuss the Basics of Urban Land Use, Including its Importance, Challenges, and Traditional Methods Used in Planning.
  • 11.2.2 Introduce Machine Learning and Its Key Concepts, Including Algorithms, Training, and Validation.

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