Intelligent Wavelet Based Techniques for Advanced Multimedia Applications

Foreword
Preface
	Outline of the Book and Chapter Synopsis
	Special Acknowledgments
Contents
About the Authors
List of Figures
List of Tables
Chapter 1: Wavelets and Intelligent Multimedia Applications: An Introduction
	1.1 Introduction
	1.2 Motivations and Objectives
	1.3 Wavelet Transforms and Intelligent Multimedia Applications
		1.3.1 Image Fusion
		1.3.2 Image Watermarking
		1.3.3 Video Watermarking
		1.3.4 Object Tracking
		1.3.5 Person Identification
		1.3.6 Activity Recognition
		1.3.7 Emotion Recognition Using Facial Expressions
	1.4 Summary
	References
Chapter 2: Wavelet Transforms: From Classical to New Generation Wavelets
	2.1 Introduction
	2.2 Wavelet Transforms
		2.2.1 Discrete Wavelet Transform
		2.2.2 Complex Wavelet Transforms
			2.2.2.1 Dual Tree Complex Wavelet Transform (DTCWT)
			2.2.2.2 Discrete Complex Wavelet Transform (DCxWT)
			2.2.2.3 Properties of Complex Wavelet Transforms
		2.2.3 Curvelet Transform
			2.2.3.1 Analysis
			2.2.3.2 Synthesis
		2.2.4 Contourlet Transform
		2.2.5 Nonsubsampled Contourlet Transform
	2.3 Summary
	References
Chapter 3: An Overview of Medical Image Fusion in Complex Wavelet Domain
	3.1 Introduction
	3.2 Background and Literature
	3.3 Methodology and Experiments
	3.4 Fusion Results and Discussions
		3.4.1 Qualitative Evaluation
		3.4.2 Quantitative Evaluation
	3.5 Summary
	References
Chapter 4: Integration of Wavelet Transforms for Single and Multiple Image Watermarking
	4.1 Introduction
	4.2 Related Work
	4.3 Theoretical Background
		4.3.1 Nonsubsampled Contourlet Transform (NSCT)
		4.3.2 Discrete Cosine Transform (DCT)
		4.3.3 Multiresolution Singular Value Decomposition (MSVD)
		4.3.4 Arnold Transform
	4.4 Methodology and Comparative Analysis
	4.5 Summary
	References
Chapter 5: On Wavelet Domain Video Watermarking Techniques
	5.1 Introduction
	5.2 Video Watermarking: An Overview of Applications and Characteristics
	5.3 Video Watermarking in Wavelet Domain: Literature Review
	5.4 Methodology and Evaluation Strategies
	5.5 Summary
	References
Chapter 6: Object Tracking
	6.1 Introduction
	6.2 Literature Analysis
		6.2.1 Sensors Based Systems
		6.2.2 Marker Based Vision Systems
		6.2.3 Marker-Less Vision Systems
		6.2.4 Robot-Guided Systems
	6.3 Significance of Curvelet in Tracking
	6.4 Experimentation and Results
		6.4.1 Single Object Tracking: Child Video Results
		6.4.2 Multiple Object Tracking: Soccer Video Results
		6.4.3 Multiple Object Tracking: Own Video Results
		6.4.4 Multiple Object Tracking: Challenge Video Results
		6.4.5 Performance Evaluation
	6.5 Summary
	References
Chapter 7: Camouflaged Person Identification
	7.1 Introduction
	7.2 Wavelet for Person Identification
	7.3 A General Algorithm
		7.3.1 Representation of Camouflaged Person Objects in Real Scenarios
		7.3.2 Two Step Preprocessing of Images
		7.3.3 Extraction of Multi-Resolution Wavelet Coefficients
		7.3.4 Wavelet Coefficients Classification
	7.4 Results and Discussion
	7.5 Summary
	References
Chapter 8: Wavelets for Activity Recognition
	8.1 Introduction
	8.2 Literature Review
	8.3 Activity Framework
		8.3.1 Input Video
		8.3.2 Preprocessing
		8.3.3 Motion Segmentation
		8.3.4 Wavelet Feature Extraction
		8.3.5 Classifier Training and Testing
	8.4 Results and Discussion
		8.4.1 Results over Weizmann Dataset
		8.4.2 Results over KTH Dataset
	8.5 Summary
	References
Chapter 9: Biometric Recognition of Emotions Using Wavelets
	9.1 Introduction
	9.2 Literature Review
		9.2.1 Basic FER Techniques
		9.2.2 Micro FER Techniques
		9.2.3 3D FER Techniques
	9.3 FER Framework
		9.3.1 Face Detection and Normalization
		9.3.2 Discrete Wavelet Coefficient Extraction
		9.3.3 Feature Selection
		9.3.4 Multiclass SVM Classifier
	9.4 Results and Discussion
		9.4.1 Experiments over the JAFFE Dataset
		9.4.2 Extended Cohn-Kanade (CK+) Dataset Results
	9.5 Summary
	References
Chapter 10: Intelligent Multimedia Applications in Wavelet Domain: New Trends and Future Research Directions
	10.1 Introduction
	10.2 Video Analytics in Wavelet Domain
		10.2.1 Single and Multiple Object Tracking
		10.2.2 Person Identification and Re-Identification
		10.2.3 Abnormal Activity Recognition
		10.2.4 Emotion Recognition
		10.2.5 Crowd Behavior Analysis
	10.3 Wavelets and Deep Learning
	10.4 Wavelets for Healthcare Monitoring
	10.5 Wavelets for Internet of Things
	10.6 Wavelets in Biology and Agriculture
	10.7 Summary
	References