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Practical Image and Video Processing Using MATLAB

Name: Practical Image and Video Processing Using MATLAB
Author: Oge Marques
ISBN: 9780470048153

by Oge Marques

September 2011

Beginner to intermediate

704 pages

15h 44m

English

Wiley-IEEE Press

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Cover
Title Page
Copyright
Dedication
List of Figures
List of Tables
Foreword
Preface
ApproachKey FeaturesA Tour of the BookNotes to Instructors
Acknowledgments
Part I: Image Processing

Chapter 1: Introduction and Overview
What Will We Learn?1.1 Motivation1.2 Basic concepts and terminology1.3 Examples of typical image processing operations1.4 Components of a Digital Image Processing System1.5 Machine Vision Systems1.6 Resources1.7 What have we learned?Learn more about it1.8 Problems
Chapter 2: Image Processing Basics
What Will We Learn?2.1 Digital Image Representation2.2 Image File Formats2.3 Basic Terminology2.4 Overview of Image Processing OperationsWhat Have We Learned?Learn more about it
Chapter 3: Matlab Basics
What will we Learn?3.1 Introduction to MATLAB3.2 Basic Elements of MATLAB3.3 Programming Tools: Scripts and Functions3.4 Graphics and Visualization3.5 Tutorial 3.1: MATLAB—a Guided Tour3.6 Tutorial 3.2: MATLAB data Structures3.7 Tutorial 3.3: Programming in MATLABWhat have we Learned?Learn More About ItOn the Web3.8 Problems
Chapter 4: The Image Processing Toolbox At a Glance
What Will We Learn?4.1 The Image Processing Toolbox: an overview4.2 Essential functions and features4.3 Tutorial 4.1: MATLAB Image Processing Toolbox—a Guided Tour4.4 Tutorial 4.2: Basic image manipulationWhat have we learned?Learn more about itOn the Web4.5 Problems
Chapter 5: Image Sensing and Acquisition
What will we learn?5.1 Introduction5.2 Light, Color, and Electromagnetic Spectrum5.3 Image Acquisition5.4 Image DigitizationWhat Have We Learned?Learn More About ItOn the Web5.5 Problems
Chapter 6: Arithmetic and Logic Operations
What will we Learn?6.1 Arithmetic Operations: Fundamentals and Applications6.2 Logic Operations: Fundamentals and Applications6.3 Tutorial 6.1: Arithmetic Operations6.4 Tutorial 6.2: Logic Operations and Region of Interest ProcessingWhat Have We Learned?6.5 problems
Chapter 7: Geometric Operations
What Will we Learn?7.1 Introduction7.2 Mapping and Affine Transformations7.3 Interpolation Methods7.4 Geometric Operations Using MATLAB7.5 Other Geometric Operations and Applications7.6 Tutorial 7.1: Image Cropping, Resizing, Flipping, and Rotation7.7 Tutorial 7.2: Spatial Transformations and Image RegistrationWhat have we Learned?Learn More About itOn the Web7.8 Problems
Chapter 8: Gray-Level Transformations
What Will We Learn?8.1 Introduction8.2 Overview of gray-level (point) transformations8.3 Examples of point transformations8.4 Specifying the transformation function8.5 Tutorial 8.1: Gray-level transformations8.6 Problems
Chapter 9: Histogram Processing
What will we learn?9.1 Image histogram: definition and example9.2 Computing image histograms9.3 Interpreting image histograms9.4 Histogram equalization9.5 Direct histogram specification9.6 Other histogram modification techniques9.7 Tutorial 9.1: Image histograms9.8 Tutorial 9.2: Histogram equalization and specification9.9 Tutorial 9.3: Other histogram modification techniquesWhat have we learned?Learn more about it9.10 Problems
Chapter 10: Neighborhood Processing
What will We Learn?10.1 Neighborhood Processing10.2 Convolution and Correlation10.3 Image Smoothing (Low-Pass Filters)10.4 Image Sharpening (High-Pass Filters)10.5 Region of Interest Processing10.6 Combining Spatial Enhancement Methods10.7 Tutorial 10.1: Convolution and Correlation10.8 Tutorial 10.2: Smoothing Filters in the Spatial Domain10.9 Tutorial 10.3: Sharpening Filters in the Spatial DomainWhat have we learned?Learn more about it10.10 Problems
Chapter 11: Frequency-Domain Filtering
11.1 IntroductionFourier Transform: the mathematical foundationLow-pass filteringHigh-pass filteringTutorial 11.1: 2D Fourier TransformTutorial 11.2: Low-pass filters in the frequency domainTutorial 11.3: High-pass filters in the frequency domainWhat have we learned?Learn more about it11.8 Problems
Chapter 12: Image Restoration
What will We Learn?12.1 Modeling of the Image Degradation and Restoration Problem12.2 Noise and Noise Models12.3 Noise Reduction Using Spatial-Domain Techniques12.4 Noise Reduction Using Frequency-Domain Techniques12.5 Image deblurring techniques12.6 Tutorial 12.1: Noise Reduction Using Spatial-Domain Techniques12.7 Problems
Chapter 13: Morphological Image Processing
What will We Learn?13.1 Introduction13.2 Fundamental concepts and operations13.3 Dilation and erosion13.4 Compound operations13.5 Morphological filtering13.6 Basic morphological algorithms13.7 Grayscale morphology13.8 Tutorial 13.1: Binary morphological image processing13.9 Tutorial 13.2: Basic morphological algorithmsWhat have we learned?Learn more about it13.10 Problems
Chapter 14: Edge Detection
What will We Learn?14.1 Formulation of the problem14.2 Basic concepts14.3 First-order derivative edge detection14.4 Second-order derivative edge detection14.5 The Canny edge detector14.6 Edge linking and boundary detection14.7 Tutorial 14.1: Edge detection14.8 Problems
Chapter 15: Image Segmentation
What Will We Learn?15.1 Introduction15.2 Intensity-Based Segmentation15.3 Region-Based Segmentation15.4 Watershed Segmentation15.5 Tutorial 15.1: Image ThresholdingWhat Have We Learned?Learn More About ItOn The Web15.6 Problems
Chapter 16: Color Image Processing
What Will we Learn?16.1 The Psychophysics of Color16.2 Color Models16.3 Representation of color images in MATLAB16.4 Pseudocolor Image Processing16.5 Full-Color Image Processing16.6 Tutorial 16.1: Pseudocolor Image Processing16.7 Tutorial 16.2: Full-Color Image ProcessingWhat have We Learned?Learn More About ItOn the Web16.8 Problems
Chapter 17: Image Compression and Coding
What will we learn?17.1 Introduction17.2 Basic concepts17.3 Lossless and lossy compression techniques17.4 Image compression standards17.5 Image quality measures17.6 Tutorial 17.1: Image compressionWhat have we learned?Learn more about itOn the Web
Chapter 18: Feature Extraction and Representation
What will we learn?18.1 Introduction18.2 Feature vectors and vector spaces18.3 Binary object features18.4 Boundary descriptors18.5 Histogram-based (statistical) features18.6 Texture features18.7 Tutorial 18.1: Feature extraction and representationWhat have we learned?Learn more about it18.8 Problems
Chapter 19: Visual Pattern Recognition
What Will We Learn?19.1 Introduction19.2 Fundamentals19.3 Statistical Pattern Classification Techniques19.4 Tutorial 19.1: Pattern classificationWhat Have We Learned?Learn More About itOn the Web19.5 Problems
Part II: Video Processing
Chapter 20: Video Fundamentals
What Will We Learn?20.1 Basic Concepts and Terminology20.2 Monochrome Analog Video20.3 Color in Video20.4 Analog Video Standards20.5 Digital Video Basics20.6 Analog-to-Digital Conversion20.7 Color Representation and Chroma Subsampling20.8 Digital Video Formats and Standards20.9 Video Compression Techniques and Standards20.10 Video processing in MATLAB20.11 Tutorial 20.1: Basic Digital Video Manipulation in MATLAB20.12 Tutorial 20.2: Working with YUV video dataWhat Have We Learned?Learn More About ItOn the Web20.13 Problems
Chapter 21: Video Sampling Rate and Standards Conversion
What Will We Learn?21.1 Video Sampling21.2 Sampling Rate Conversion21.3 Standards Conversion21.4 Tutorial 21.1: Line Down-Conversion21.5 Tutorial 21.2: Deinterlacing21.6 Tutorial 21.3: NTSC to PAL Conversion21.7 Tutorial 21.4: 3:2 Pull-DownWhat Have We learned?Learn More About It21.8 Problems
Chapter 22: Digital Video Processing Techniques and Applications
What Will We Learn?22.1 Fundamentals of Motion Estimation and Motion Compensation22.2 General Methodologies in Motion Estimation22.3 Motion Estimation Algorithms22.4 Video Enhancement and Noise Reduction22.5 Case Study: Object Segmentation and Tracking in the Presence of Complex Background22.6 Tutorial 22.1: Block-based Motion Estimation22.7 Tutorial 22.2: Intraframe and Interframe Filtering TechniquesWhat Have We Learned?Learn More About It22.8 Problems
Appendix A: Human Visual Perception
A.1 IntroductionA.2 The Human EyeA.3 Characteristics of Human VisionA.4 Implications and Applications of Knowledge about the Human Visual SystemLearn More About ItOn the Web
Appendix B: Gui Development
B.1 IntroductionB.2 GUI File StructureB.3 Passing System ControlB.4 The UserData ObjectB.5 A Working GUI DemoB.6 Concluding Remarks
References
Index

Content preview from Practical Image and Video Processing Using MATLAB

Chapter 4

The Image Processing Toolbox at a glance

What Will We Learn?

How do I read an image from a file using MATLAB?
What are the main data classes used for image representation and how can I convert from one to another?
Why is it important to understand the data class and range of pixel values of images stored in memory?
How do I display an image using MATLAB?
How can I explore the pixel contents of an image?
How do I save an image to a file using MATLAB?

4.1 The Image Processing Toolbox: an overview

The Image Processing Toolbox (IPT) is a collection of functions that extend the basic capability of the MATLAB environment to enable specialized signal and image processing operations, such as the following:

Spatial transformations (Chapter 7)
Image analysis and enhancement (Chapters 08–12, 14–15, and 18)
Neighborhood and block operations (Chapter 10)
Linear filtering and filter design (Chapters 10 and 11)
Mathematical transforms (Chapters 11 and 17)
Deblurring (Chapter 12)
Morphological operations (Chapter 13)
Color image processing (Chapter 16)

Most of the toolbox functions are MATLAB M-files, whose source code can be inspected by opening the file using the MATLAB editor or simply typing type function_name at the prompt. The directory in which the M-file has been saved can be found by typing which function_name at the prompt. To determine which version of the IPT is installed on your system, type ver at the prompt.

You can extend the capabilities of the IPT by writing your ...

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Practical Image and Video Processing Using MATLAB

by Oge Marques

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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