Algorithms for Image Processing and Computer Vision - 2nd Edition

Informationen zum Autor J. R. Parker is a full professor working in the Art department at the University of Calgary. His major research projects include live performance in online virtual spaces, the design and construction of kinetic games, and the portrayal of Canadian history and culture in digital and online form. Klappentext A cookbook of algorithms for common image processing applicationsThanks 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. Zusammenfassung Programmers, scientists, and engineers are always in need of newer techniques and algorithms to manipulate and interpret images. Algorithms for Image Processing and Computer Vision is an accessible collection of algorithms for common image processing applications that simplifies complicated mathematical calculations. Inhaltsverzeichnis Preface xxi Chapter 1 Practical Aspects of a Vision System - Image Display, Input/Output, and Library Calls 1 OpenCV 2 The Basic OpenCV Code 2 The IplImage Data Structure 3 Reading and Writing Images 6 Image Display 7 An Example 7 Image Capture 10 Interfacing with the AIPCV Library 14 Website Files 18 References 18 Chapter 2 Edge-Detection Techniques 21 The Purpose of Edge Detection 21 Traditional Approaches and Theory 23 Models of Edges 24 Noise 26 Derivative Operators 30 Template-Based Edge Detection 36 Edge Models: The Marr-Hildreth Edge Detector 39 The Canny Edge Detector 42 The Shen-Castan (ISEF) Edge Detector 48 A Comparison of Two Optimal Edge Detectors 51 Color Edges 53 Source Code for the Marr-Hildreth Edge Detector 58 Source Code for the Canny Edge Detector 62 Source Code for the Shen-Castan Edge Detector 70 Website Files 80 References 82 Chapter 3 Digital Morphology 85 Morphology Defined 85 Connectedness 86 Elements of Digital Morphology - Binary Operations 87 Binary Dilation 88 Implementing Binary Dilation 92 Binary Erosion 94 Implementation of Binary Erosion 100 Opening and Closing 101 MAX - A High-Level Programming Language for Morphology 107 The ''Hit-and-Miss'' Transform 113 Identifying Region Boundaries 116 Conditional Dilation 116 Counting Regions 119 Grey-Level Morphology 121 Opening and Closing 123 Smoothing 126 Gradient 128 Segmentation of Textures 129 Size Distribution of Objects 130 Color Morphology 131 Website F...

List of contents

Preface.
 
Chapter 1 Practical Aspects of a Vision System -- Image Display, Input/Output, and Library Calls.
 
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 Algorit

Product details

• Authors: J J Parker, J R Parker, J. R. Parker, JR Parker, Parker J. R.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 30.12.2010
• EAN: 9780470643853
• ISBN: 978-0-470-64385-3
• No. of pages: 504
• Dimensions: 189 mm x 235 mm x 26 mm
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Application software; Informatik, computer science, algorithms and data structures, Computergraphik, Visualization & Computer Graphics, Visualisierung u. Computergraphik