Algorithms for Image Processing and Computer Vision 2nd Edition by Parker 0470643854 978-0470643853

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ISBN 10: 0470643854

ISBN 13: 978-0470643853

Author: J. R. Parker

applications

Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing.

• Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists
• This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids
• Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications.

Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.

Algorithms for Image Processing and Computer Vision 2nd Table of contents:

Chapter 1: Practical Aspects of a Vision System

OpenCV

The Basic OpenCV Code

The IplImage Data Structure

Reading and Writing Images

Image Display

An Example

Image Capture

Interfacing with the AIPCV Library

Website Files

References

Chapter 2: Edge-Detection Techniques

The Purpose of Edge Detection

Traditional Approaches and Theory

Models of Edges

Noise

Derivative Operators

Template-Based Edge Detection

Edge Models: The Marr-Hildreth Edge Detector

The Canny Edge Detector

The Shen-Castan (ISEF) Edge Detector

A Comparison of Two Optimal Edge Detectors

Color Edges

Source Code for the Marr-Hildreth Edge Detector

Source Code for the Canny Edge Detector

Source Code for the Shen-Castan Edge Detector

Website Files

References

Chapter 3: Digital Morphology

Morphology Defined

Connectedness

Elements of Digital Morphology — Binary Operations

Binary Dilation

Implementing Binary Dilation

Binary Erosion

Implementation of Binary Erosion

Opening and Closing

MAX — A High-Level Programming Language for Morphology

The “Hit-and-Miss” Transform

Identifying Region Boundaries

Conditional Dilation

Counting Regions

Grey-Level Morphology

Opening and Closing

Smoothing

Gradient

Segmentation of Textures

Size Distribution of Objects

Color Morphology

Website Files

References

Chapter 4: Grey-Level Segmentation

Basics of Grey-Level Segmentation

Using Edge Pixels

Iterative Selection

The Method of Grey-Level Histograms

Using Entropy

Fuzzy Sets

Minimum Error Thresholding

Sample Results From Single Threshold Selection

The Use of Regional Thresholds

Chow and Kaneko

Modeling Illumination Using Edges

Implementation and Results

Comparisons

Relaxation Methods

Moving Averages

Cluster-Based Thresholds

Multiple Thresholds

Website Files

References

Chapter 5: Texture and Color

Texture and Segmentation

A Simple Analysis of Texture in Grey-Level Images

Grey-Level Co-Occurrence

Maximum Probability

Moments

Contrast

Homogeneity

Entropy

Results from the GLCM Descriptors

Speeding Up the Texture Operators

Edges and Texture

Energy and Texture

Surfaces and Texture

Vector Dispersion

Surface Curvature

Fractal Dimension

Color Segmentation

Color Textures

Website Files

References

Chapter 6: Thinning

What Is a Skeleton?

The Medial Axis Transform

Iterative Morphological Methods

The Use of Contours

Choi/Lam/Siu Algorithm

Treating the Object as a Polygon

Triangulation Methods

Force-Based Thinning

Definitions

Use of a Force Field

Subpixel Skeletons

Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm

Website Files

References

Chapter 7: Image Restoration

Image Degradations — The Real World

The Frequency Domain

The Fourier Transform

The Fast Fourier Transform

The Inverse Fourier Transform

Two-Dimensional Fourier Transforms

Fourier Transforms in OpenCV

Creating Artificial Blur

The Inverse Filter

The Wiener Filter

Structured Noise

Motion Blur — A Special Case

The Homomorphic Filter — Illumination

Frequency Filters in General

Isolating Illumination Effects

Website Files

References

Chapter 8: Classification

Objects, Patterns, and Statistics

Features and Regions

Training and Testing

Variation: In-Class and Out-Class

Minimum Distance Classifiers

Distance Metrics

Distances Between Features

Cross Validation

Support Vector Machines

Multiple Classifiers — Ensembles

Merging Multiple Methods

Merging Type 1 Responses

Evaluation

Converting Between Response Types

Merging Type 2 Responses

Merging Type 3 Responses

Bagging and Boosting

Website Files

References

Chapter 9: Symbol Recognition

The Problem

OCR on Simple Perfect Images

OCR on Scanned Images — Segmentation

Noise

Isolating Individual Glyphs

Matching Templates

Statistical Recognition

OCR on Fax Images — Printed Characters

Orientation — Skew Detection

The Use of Edges

Handprinted Characters

Properties of the Character Outline

Convex Deficiencies

Vector Templates

Neural Nets

A Simple Neural Net

A Backpropagation Net for Digit Recognition

The Use of Multiple Classifiers

Merging Multiple Methods

Results From the Multiple Classifier

Printed Music Recognition — A Study

Source Code for Neural Net Recognition System

Website Files

References

Chapter 10: Content-Based Search — Finding Images by Example

Searching Images

Maintaining Collections of Images

Features for Query by Example

Color Image Features

Mean Color

Color Quad Tree

Hue and Intensity Histograms

Comparing Histograms

Requantization

Results from Simple Color Features

Other Color-Based Methods

Grey-Level Image Features

Grey Histograms

Edge Density — Boundaries Between Objects

Spatial Considerations

Texture

Data Sets

Website Files

References

Chapter 11: High-Performance Computing for Vision and Image Processing

Paradigms for Multiple-Processor Computation

Shared Memory

Message Passing

Execution Timing

Using clock()

Using QueryPerformanceCounter

The Message-Passing Interface System

Installing MPI

Using MPI

Inter-Process Communication

Real Image Computations

Using a Computer Network — Cluster Computing

A Shared Memory System — Using the PC Graphics Processor

GLSL

OpenGL Fundamentals

Practical Textures in OpenGL

Shader Programming Basics

Vertex and Fragment Shaders

Required GLSL Initializations

Reading and Converting the Image

Speedup Using the GPU

Developing and Testing Shader Code

Finding the Needed Software

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