Nonlinear Model-Based Image/Video Processing and Analysis
A comprehensive survey of techniques and applications in image and video processing and analysis A widely varied selection of experts provides extensive coverage of nonlinear model-based techniques in image and video processing and analysis. This volume not only details new techniques in still image and digital video but also discusses applications in computer vision, multimedia, and visual information retrieval systems. All nonlinear, model-based techniques are detailed, and a complete and up-to-date accounting of the most important and effective algorithms is included for every application and technique discussed. The applications described also include such real-world uses as multimedia information retrieval, automatic surveillance, lip tracking, and much more. Analytical techniques such as Boolean and stack filters, rational functions, mathematical morphology techniques, and adaptive order statistic filtering receive detailed treatment as they apply to specific uses such as image compression, segmentation, old motion picture restoration, image/video interpolation, and noise smoothing. Chapters include: Optimal Design of Boolean and Stack Filters and Their Application in Image Processing Image Processing Using Rational Functions Mathematical Morphology and Motion Picture Restoration Adaptive Order Statistic Filtering of Still Images and Image Sequences Video Segmentation Based on Multiple Features for Interactive and Automatic Multimedia Applications Invariant Features in Pattern Recognition-Fundamentals and Applications Image Models for Facial Feature Tracking Students, engineers, and scientists will appreciate both the wide range of expert opinion in this comprehensive volume and the detailed coverage of the algorithms and mathematical models used in state-of-the-art image and video processing.
Why Read This Book
You should read this book if you need a broad, model-oriented tour of nonlinear techniques used in image and video processing; it collects expert treatments of MRFs, variational/PDE approaches, morphological and robust nonlinear methods and ties them to real applications such as tracking and retrieval. You will gain practical and conceptual insights that bridge classical DSP ideas and more modern model-based vision algorithms.
Who Will Benefit
Advanced graduate students, research engineers, and practitioners in signal/image processing or computer vision who want model-based nonlinear methods for denoising, segmentation, tracking, and multimedia retrieval.
Level: Advanced — Prerequisites: Solid background in linear signal processing, basic probability/statistics, and introductory image processing (familiarity with filtering, convolution, and basic segmentation concepts). Some comfort with MATLAB or prototyping in high-level languages is helpful.
Key Takeaways
- Understand different nonlinear model classes (e.g., Markov Random Fields, variational/PDE models, morphological models) and when to apply them.
- Apply variational and PDE-based methods for image denoising, restoration, and edge-preserving smoothing.
- Design and implement model-based segmentation and tracking algorithms for video sequences.
- Use statistical and robust estimation techniques within nonlinear frameworks for practical problems such as surveillance and lip tracking.
- Evaluate model-based methods for multimedia retrieval and visual information indexing in applied systems.
Topics Covered
- Preface and overview of nonlinear model-based processing
- Foundations: probability, energy models, and Bayesian estimation
- Markov Random Fields and Gibbs models for image analysis
- Variational methods and PDE-based image processing
- Morphological and rank-order nonlinear filtering
- Nonlinear diffusion and anisotropic smoothing
- Wavelet and multiscale nonlinear approaches
- Model-based segmentation techniques
- Motion estimation and video tracking using nonlinear models
- Statistical methods for texture and pattern analysis
- Applications: surveillance, lip tracking, and multimedia retrieval
- Performance evaluation and datasets
- Conclusions and future directions
Languages, Platforms & Tools
How It Compares
Complementary to Gonzalez & Woods' Digital Image Processing (which is broader and more introductory), this volume focuses specifically on nonlinear, model-based methods and applications; it also overlaps with specialized texts on MRFs and energy-based vision methods but offers a wider collection of application-driven chapters.
