Digital Image Processing Second Edition By Rafael C. Gonzalez And Richard E. Woods

Digital Image Processing book pdf

Download Free Digital Image Processing Second Edition Pdf

Introduction: Digital Image Processing book pdf

Download Digital Image Processing Book Pdf Second Edition, Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.This chapter has several objectives:

(1) to define the scope of the field that we call image processing.
(2) to give a historical perspective of the origins of this field.
(3) to give an idea of the state of the art in image processing by examining some of the principal areas in which it is applied.
(4) to discuss briefly the principal approaches used in digital image processing.
(5) to give an overview of the components contained in a typical, general-purpose image processing system.
(6) to provide direction to the books and other literature where image processing work normally is reported.

What Is Digital Image Processing?
An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point.When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. We consider these definitions in more formal terms in Chapter 2.

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Table Of Contents For Digital Image Processing Book Pdf

1 Introduction

What Is Digital Image Processing?
The Origins of Digital Image Processing
Examples of Fields that Use Digital Image Processing
Gamma-Ray Imaging
X-ray Imaging
Imaging in the Ultraviolet Band
Imaging in the Visible and Infrared Bands
Imaging in the Microwave Band
Imaging in the Radio Band
Examples in which Other Imaging Modalities Are Used
Fundamental Steps in Digital Image Processing
Components of an Image Processing System
Summary
References and Further Reading

2 Digital Image Fundamentals

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Elements of Visual Perception
Structure of the Human Eye
Image Formation in the Eye
Brightness Adaptation and Discrimination
Light and the Electromagnetic Spectrum
Image Sensing and Acquisition
Image Acquisition Using a Single Sensor
Image Acquisition Using Sensor Strips
A Simple Image Formation Model
Image Sampling and Quantization
Basic Concepts in Sampling and Quantization
Representing Digital Images
Spatial and Gray-Level Resolution
Aliasing and Moiré Patterns
Zooming and Shrinking Digital Images
Some Basic Relationships Between Pixels
Neighbors of a Pixel
Adjacency, Connectivity, Regions, and Boundaries
Distance Measures
Image Operations on a Pixel Basis
Linear and Nonlinear Operations
Summary
References and Further Reading
Problems

3 Image Enhancement in the Spatial Domain

Background
Some Basic Gray Level Transformations
Image Negatives
Log Transformations
Power-Law Transformations
Piecewise-Linear Transformation Functions
Histogram Processing
Histogram Equalization
Histogram Matching (Specification)
Local Enhancement
Use of Histogram Statistics for Image Enhancement
Enhancement Using Arithmetic/Logic Operations
Image Subtraction
Image Averaging
Basics of Spatial Filtering
Smoothing Linear Filters
Order-Statistics Filters
Sharpening Spatial Filters
Foundation
Use of Second Derivatives for Enhancement–
The Laplacian
Use of First Derivatives for Enhancement—The Gradient
Combining Spatial Enhancement Methods
Summary
References and Further Reading
Problems

4 Image Enhancement in the Frequency Domain

Background
Introduction to the Fourier Transform and the Frequency Domain
The One-Dimensional Fourier Transform and its Inverse
The Two-Dimensional DFT and Its Inverse
Filtering in the Frequency Domain
Correspondence between Filtering in the Spatial and Frequency Domains
Smoothing Frequency-Domain Filters
Ideal Lowpass Filters
Butterworth Lowpass Filters
Gaussian Lowpass Filters
Additional Examples of Lowpass Filtering
Sharpening Frequency Domain Filters
Ideal Highpass Filters
Butterworth Highpass Filters
Gaussian Highpass Filters
The Laplacian in the Frequency Domain
Unsharp Masking, High-Boost Filtering, and High-Frequency Emphasis Filtering
Homomorphic Filtering
Implementation
Some Additional Properties of the 2-D Fourier Transform
Computing the Inverse Fourier Transform Using a Forward Transform Algorithm
More on Periodicity: the Need for Padding
The Convolution and Correlation Theorems
Summary of Properties of the 2-D Fourier Transform
The Fast Fourier Transform
Some Comments on Filter Design
Summary
References
Problems

5 Image Restoration

A Model of the Image Degradation/Restoration Process
Noise Models
Spatial and Frequency Properties of Noise
Some Important Noise Probability Density Functions
Periodic Noise
Estimation of Noise Parameters
Restoration in the Presence of Noise Only–Spatial Filtering
Mean Filters
Order-Statistics Filters

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Adaptive Filters
Periodic Noise Reduction by Frequency Domain Filtering
Bandreject Filters
Bandpass Filters
Notch Filters
Optimum Notch Filtering
Linear, Position-Invariant Degradations
Estimating the Degradation Function
Estimation by Image Observation
Estimation by Experimentation
Estimation by Modeling
Inverse Filtering
Minimum Mean Square Error (Wiener) Filtering
Constrained Least Squares Filtering
Geometric Mean Filter
Geometric Transformations
Spatial Transformations
Gray-Level Interpolation
Summary
References and Further Reading
Problems

6 Color Image Processing

Color Fundamentals
Color Models
The RGB Color Model
The CMY and CMYK Color Models
The HSI Color Model
Pseudocolor Image Processing
Intensity Slicing
Gray Level to Color Transformations
Basics of Full-Color Image Processing
Color Transformations
Formulation
Color Complements
Color Slicing
Tone and Color Corrections
Histogram Processing
Smoothing and Sharpening
Color Image Smoothing
Color Image Sharpening
Color Segmentation
Segmentation in HSI Color Space
Segmentation in RGB Vector Space
Color Edge Detection
Noise in Color Images
Color Image Compression
Summary
References and Further Reading
Problems

7 Wavelets and Multiresolution Processing

Background
Image Pyramids
Subband Coding
The Haar Transform
Multiresolution Expansions
Series Expansions
Scaling Functions
Wavelet Functions
Wavelet Transforms in One Dimension
The Wavelet Series Expansions
The Discrete Wavelet Transform
The Continuous Wavelet Transform
The Fast Wavelet Transform
Wavelet Transforms in Two Dimensions
Wavelet Packets
Summary
References and Further Reading
Problems

8 Image Compression

Fundamentals
Coding Redundancy
Interpixel Redundancy
Psychovisual Redundancy
Fidelity Criteria
Image Compression Models
The Source Encoder and Decoder
The Channel Encoder and Decoder
Elements of Information Theory
Measuring Information
The Information Channel
Fundamental Coding Theorems
Using Information Theory
Error-Free Compression
LZW Coding
Bit-Plane Coding
Lossless Predictive Coding
Lossy Compression
Lossy Predictive Coding
Transform Coding
Wavelet Coding
Image Compression Standards
Binary Image Compression Standards
Continuous Tone Still Image Compression Standards
Video Compression Standards
Summary
References and Further Reading
Problems

9 Morphological Image Processing

Preliminaries
Some Basic Concepts from Set Theory
Logic Operations Involving Binary Images
Dilation and Erosion
Dilation
Erosion
Opening and Closing
The Hit-or-Miss Transformation
Some Basic Morphological Algorithms
Boundary Extraction
Region Filling
Extraction of Connected Components
Convex Hull
Thinning
Thickening
Skeletons
Pruning
Summary of Morphological Operations on Binary Images
Extensions to Gray-Scale Images
Dilation
Erosion
Opening and Closing
Some Applications of Gray-Scale Morphology
Summary
References and Further Reading
Problems

10 Image Segmentation

Detection of Discontinuities
Point Detection
Line Detection
Edge Detection
Edge Linking and Boundary Detection
Local Processing

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Global Processing via the Hough Transform
Global Processing via Graph-Theoretic Techniques
Thresholding
Foundation
The Role of Illumination
Basic Global Thresholding
Basic Adaptive Thresholding
Optimal Global and Adaptive Thresholding
Use of Boundary Characteristics for Histogram Improvement and Local Thresholding
Thresholds Based on Several Variables
Region-Based Segmentation
Basic Formulation
Region Growing
Region Splitting and Merging
Segmentation by Morphological Watersheds
Basic Concepts
Dam Construction
Watershed Segmentation Algorithm
The Use of Markers
The Use of Motion in Segmentation Spatial Techniques
Frequency Domain Techniques
Summary
References and Further Reading
Problems

11 Representation and Description

Representation
Chain Codes
Polygonal Approximations
Signatures
Boundary Segments
Skeletons
Boundary Descriptors
Some Simple Descriptors
Shape Numbers
Fourier Descriptors
Statistical Moments
Regional Descriptors
Some Simple Descriptors
Topological Descriptors
Texture

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Moments of Two-Dimensional Functions
Use of Principal Components for Description
Relational Descriptors
Summary
References and Further Reading
Problems

12 Object Recognition

Patterns and Pattern Classes
Recognition Based on Decision-Theoretic Methods

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Matching
Optimum Statistical Classifiers
Neural Networks
Structural Methods
Matching Shape Numbers
String Matching
Syntactic Recognition of Strings
Syntactic Recognition of Trees
Summary
References and Further Reading
Problems
Bibliography
Index

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