Wavelet Theory And Its Application To Pattern Recognition 36 Series In Machine Perception And Artificial Intelligence 1st edition by Yuan Yan Tang, Jiming Liu, Lihua Yang, Hong Ma ISBN 9810238193  978-9810238193

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ISBN 10: 9810238193
ISBN 13: 978-9810238193
Author:  Yuan Yan Tang, Jiming Liu, Lihua Yang, Hong Ma  

This is not a purely mathematical book. It presents the basic principle of wavelet theory to electrical and electronic engineers, computer scientists, and students, as well as the ideas of how wavelets can be applied to pattern recognition. It also contains many novel research results from the authors’ research team.

Wavelet Theory And Its Application To Pattern Recognition: 36 (Series In Machine Perception And Artificial Intelligence) 1st Table of contents:

Preface

• Motivation for Wavelet Theory in Pattern Recognition
• Overview of the Book’s Structure and Focus
• Target Audience

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Part I: Foundations of Wavelet Theory

1. Introduction to Wavelet Theory

   • What is a Wavelet?
   • History and Development of Wavelets
   • Wavelet Transforms and Their Mathematical Foundations
   • Comparison Between Fourier Transform and Wavelet Transform
   • Basic Wavelet Properties: Scalability, Localization, and Regularity

2. Mathematical Foundations of Wavelets

   • Functions and Spaces: L², Sobolev Spaces, and Wavelet Basis
   • Continuous vs. Discrete Wavelet Transform (CWT and DWT)
   • Multiresolution Analysis (MRA)
   • The Haar, Daubechies, and Other Common Wavelets
   • Sampling Theorems and Reconstruction

3. Wavelet Transforms in Signal and Image Processing

   • Time-Frequency Representations
   • Image Decomposition and Reconstruction with Wavelets
   • Filtering and Denoising with Wavelet Transforms
   • Compression Techniques Using Wavelets

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Part II: Wavelet-based Methods for Pattern Recognition

4. Pattern Recognition Overview

   • Basic Concepts in Pattern Recognition
   • Feature Extraction and Selection in Pattern Recognition
   • Classification Methods: Nearest Neighbor, SVM, and Neural Networks
   • Challenges in Pattern Recognition: Noise, Variability, and High Dimensionality

5. Wavelet-Based Feature Extraction

   • Wavelet Coefficients as Features for Classification
   • Multiscale Feature Extraction: Time, Frequency, and Spatial Domains
   • Feature Vector Construction from Wavelet Transform
   • Application of Wavelet Features in Classification Tasks

6. Wavelets in Image Recognition

   • Use of Wavelet Transforms in Image Recognition
   • Texture Classification and Recognition using Wavelets
   • Wavelet-Based Edge Detection and Segmentation
   • Multi-Scale Image Features for Object Recognition
   • Applications in Medical Imaging and Remote Sensing

7. Wavelet-Based Speech and Signal Recognition

   • Signal Preprocessing and Feature Extraction
   • Wavelet Transforms for Speech Signal Analysis
   • Noise Reduction and Signal Enhancement with Wavelets
   • Applications in Speech Recognition and Audio Classification
   • Case Studies in Acoustic Signal Processing

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Part III: Advanced Wavelet Techniques and Applications

8. Wavelet Neural Networks

   • Combining Wavelets and Neural Networks
   • Wavelet Transform as an Input Preprocessing Layer
   • Training Wavelet Neural Networks
   • Applications of Wavelet Neural Networks in Pattern Recognition

9. Wavelet Packets and Their Application in Pattern Recognition

   • Introduction to Wavelet Packets
   • Decomposition and Reconstruction with Wavelet Packets
   • Wavelet Packet Transform for Signal and Image Recognition
   • Comparison of Wavelet Packets with Standard Wavelets in Pattern Recognition

10. Multidimensional Wavelet Transforms

    • Multidimensional Wavelets: Theory and Algorithms
    • Applications in 2D and 3D Image and Video Processing
    • Medical Imaging and 3D Object Recognition
    • Applications in Texture Recognition and Surface Detection

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Part IV: Case Studies and Applications

11. Wavelet Methods in Biomedical Applications

    • Applications of Wavelet Transforms in Medical Imaging
    • Wavelet-Based Feature Extraction in MRI and CT Scans
    • Detection of Abnormalities and Tumors Using Wavelet Transforms
    • Electrocardiogram (ECG) Signal Classification with Wavelets

12. Wavelet-Based Applications in Remote Sensing

    • Satellite Image Classification and Feature Extraction
    • Wavelet Transforms for Geospatial Data Analysis
    • Texture and Object Recognition in Remote Sensing Data
    • Environmental Monitoring Using Wavelet Methods

13. Wavelet Transforms in Video and Motion Analysis

    • Video Compression and Transmission with Wavelet Transforms
    • Motion Detection and Object Tracking in Video
    • Applications in Surveillance and Security
    • Gesture and Activity Recognition Using Wavelet Features

14. Wavelets in Industrial and Engineering Applications

    • Quality Control and Fault Detection Using Wavelets
    • Signal Analysis in Vibration and Acoustic Signals
    • Wavelet-Based Techniques for Predictive Maintenance
    • Applications in Machine Monitoring and Fault Diagnosis

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Part V: Future Directions and Emerging Trends

15. Future Challenges in Wavelet-based Pattern Recognition

    • Scalability and Real-Time Processing of Wavelet Transforms
    • Robustness and Adaptability of Wavelet Methods
    • The Role of Wavelets in Big Data and High-Dimensional Analysis
    • Emerging Wavelet-Based Techniques in AI and Deep Learning

16. Wavelets and Deep Learning: A Synergistic Approach

    • Combining Wavelet Transforms with Convolutional Neural Networks (CNNs)
    • Wavelets for Data Preprocessing and Feature Learning
    • Applications in Large-Scale Pattern Recognition Tasks
    • Hybrid Models for Multimodal Data Processing

17. Wavelet Transform Applications in Emerging Fields

    • Wavelets in Quantum Computing and Quantum Information
    • Advanced Applications in Cognitive Computing and AI
    • Wavelets for Real-Time Sensor Networks and IoT Devices
    • Potential Applications in Autonomous Systems and Robotics

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Conclusion

• Summary of Key Concepts in Wavelet Theory and Pattern Recognition
• Future of Wavelet Applications in AI and Machine Perception
• Final Thoughts and Next Steps for Researchers and Practitioners

References

• Citations of Key Research Papers, Books, and Articles

Index

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