Book description
Up-to-date, technically accurate coverage of essential topics in image and video processing
This is the first book to combine image and video processing with a practical MATLAB®-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation.
The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation.
Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®.
Extra features of this book include:
More than 30 MATLAB® tutorials, which consist of step-by-step guides to exploring image and video processing techniques using MATLAB®
Chapters supported by figures, examples, illustrative problems, and exercises
Useful websites and an extensive list of bibliographical references
This accessible text is ideal for upper-level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.
Table of contents
- Cover
- Title Page
- Copyright
- Dedication
- List of Figures
- List of Tables
- Foreword
- Preface
- Acknowledgments
-
Part I: Image Processing
- Chapter 1: Introduction and Overview
- Chapter 2: Image Processing Basics
-
Chapter 3: Matlab Basics
- What will we Learn?
- 3.1 Introduction to MATLAB
- 3.2 Basic Elements of MATLAB
- 3.3 Programming Tools: Scripts and Functions
- 3.4 Graphics and Visualization
- 3.5 Tutorial 3.1: MATLAB—a Guided Tour
- 3.6 Tutorial 3.2: MATLAB data Structures
- 3.7 Tutorial 3.3: Programming in MATLAB
- What have we Learned?
- Learn More About It
- On the Web
- 3.8 Problems
- Chapter 4: The Image Processing Toolbox at a Glance
- Chapter 5: Image Sensing and Acquisition
- Chapter 6: Arithmetic and Logic Operations
-
Chapter 7: Geometric Operations
- What Will we Learn?
- 7.1 Introduction
- 7.2 Mapping and Affine Transformations
- 7.3 Interpolation Methods
- 7.4 Geometric Operations Using MATLAB
- 7.5 Other Geometric Operations and Applications
- 7.6 Tutorial 7.1: Image Cropping, Resizing, Flipping, and Rotation
- 7.7 Tutorial 7.2: Spatial Transformations and Image Registration
- What have we Learned?
- Learn More About it
- On the Web
- 7.8 Problems
- Chapter 8: Gray-Level Transformations
-
Chapter 9: Histogram Processing
- What will we learn?
- 9.1 Image histogram: definition and example
- 9.2 Computing image histograms
- 9.3 Interpreting image histograms
- 9.4 Histogram equalization
- 9.5 Direct histogram specification
- 9.6 Other histogram modification techniques
- 9.7 Tutorial 9.1: Image histograms
- 9.8 Tutorial 9.2: Histogram equalization and specification
- 9.9 Tutorial 9.3: Other histogram modification techniques
- What have we learned?
- Learn more about it
- 9.10 Problems
-
Chapter 10: Neighborhood Processing
- What will We Learn?
- 10.1 Neighborhood Processing
- 10.2 Convolution and Correlation
- 10.3 Image Smoothing (Low-Pass Filters)
- 10.4 Image Sharpening (High-Pass Filters)
- 10.5 Region of Interest Processing
- 10.6 Combining Spatial Enhancement Methods
- 10.7 Tutorial 10.1: Convolution and Correlation
- 10.8 Tutorial 10.2: Smoothing Filters in the Spatial Domain
- 10.9 Tutorial 10.3: Sharpening Filters in the Spatial Domain
- What have we learned?
- Learn more about it
- 10.10 Problems
-
Chapter 11: Frequency-Domain Filtering
- 11.1 Introduction
- 11.2 Fourier Transform: the mathematical foundation
- 11.3 Low-pass filtering
- 11.4 High-pass filtering
- 11.5 Tutorial 11.1: 2D Fourier Transform
- 11.6 Tutorial 11.2: Low-pass filters in the frequency domain
- 11.7 Tutorial 11.3: High-pass filters in the frequency domain
- What have we learned?
- Learn more about it
- 11.8 Problems
-
Chapter 12: Image Restoration
- What will We Learn?
- 12.1 Modeling of the Image Degradation and Restoration Problem
- 12.2 Noise and Noise Models
- 12.3 Noise Reduction Using Spatial-Domain Techniques
- 12.4 Noise Reduction Using Frequency-Domain Techniques
- 12.5 Image deblurring techniques
- 12.6 Tutorial 12.1: Noise Reduction Using Spatial-Domain Techniques
- 12.7 Problems
-
Chapter 13: Morphological Image Processing
- What will We Learn?
- 13.1 Introduction
- 13.2 Fundamental concepts and operations
- 13.3 Dilation and erosion
- 13.4 Compound operations
- 13.5 Morphological filtering
- 13.6 Basic morphological algorithms
- 13.7 Grayscale morphology
- 13.8 Tutorial 13.1: Binary morphological image processing
- 13.9 Tutorial 13.2: Basic morphological algorithms
- What have we learned?
- Learn more about it
- 13.10 Problems
- Chapter 14: Edge Detection
- Chapter 15: Image Segmentation
-
Chapter 16: Color Image Processing
- What Will we Learn?
- 16.1 The Psychophysics of Color
- 16.2 Color Models
- 16.3 Representation of color images in MATLAB
- 16.4 Pseudocolor Image Processing
- 16.5 Full-Color Image Processing
- 16.6 Tutorial 16.1: Pseudocolor Image Processing
- 16.7 Tutorial 16.2: Full-Color Image Processing
- What have We Learned?
- Learn More About It
- On the Web
- 16.8 Problems
- Chapter 17: Image Compression and Coding
-
Chapter 18: Feature Extraction and Representation
- What will we learn?
- 18.1 Introduction
- 18.2 Feature vectors and vector spaces
- 18.3 Binary object features
- 18.4 Boundary descriptors
- 18.5 Histogram-based (statistical) features
- 18.6 Texture features
- 18.7 Tutorial 18.1: Feature extraction and representation
- What have we learned?
- Learn more about it
- 18.8 Problems
- Chapter 19: Visual Pattern Recognition
-
Part II: Video Processing
-
Chapter 20: Video Fundamentals
- What Will We Learn?
- 20.1 Basic Concepts and Terminology
- 20.2 Monochrome Analog Video
- 20.3 Color in Video
- 20.4 Analog Video Standards
- 20.5 Digital Video Basics
- 20.6 Analog-to-Digital Conversion
- 20.7 Color Representation and Chroma Subsampling
- 20.8 Digital Video Formats and Standards
- 20.9 Video Compression Techniques and Standards
- 20.10 Video processing in MATLAB
- 20.11 Tutorial 20.1: Basic Digital Video Manipulation in MATLAB
- 20.12 Tutorial 20.2: Working with YUV video data
- What Have We Learned?
- Learn More About It
- On the Web
- 20.13 Problems
-
Chapter 21: Video Sampling Rate and Standards Conversion
- What Will We Learn?
- 21.1 Video Sampling
- 21.2 Sampling Rate Conversion
- 21.3 Standards Conversion
- 21.4 Tutorial 21.1: Line Down-Conversion
- 21.5 Tutorial 21.2: Deinterlacing
- 21.6 Tutorial 21.3: NTSC to PAL Conversion
- 21.7 Tutorial 21.4: 3:2 Pull-Down
- What Have We learned?
- Learn More About It
- 21.8 Problems
-
Chapter 22: Digital Video Processing Techniques and Applications
- What Will We Learn?
- 22.1 Fundamentals of Motion Estimation and Motion Compensation
- 22.2 General Methodologies in Motion Estimation
- 22.3 Motion Estimation Algorithms
- 22.4 Video Enhancement and Noise Reduction
- 22.5 Case Study: Object Segmentation and Tracking in the Presence of Complex Background
- 22.6 Tutorial 22.1: Block-based Motion Estimation
- 22.7 Tutorial 22.2: Intraframe and Interframe Filtering Techniques
- What Have We Learned?
- Learn More About It
- 22.8 Problems
-
Chapter 20: Video Fundamentals
- Appendix A: Human Visual Perception
- Appendix B: Gui Development
- References
- Index
Product information
- Title: Practical Image and Video Processing Using MATLAB®
- Author(s):
- Release date: September 2011
- Publisher(s): Wiley-IEEE Press
- ISBN: 9780470048153
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