Algorithms for Image Processing and Computer Vision, 2nd Edition
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.
Chapter 1 Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls.
The Basic OpenCV Code.
The IplImage Data Structure.
Reading and Writing Images.
Interfacing with the AIPCV Library.
Chapter 2 Edge-Detection Techniques.
The Purpose of Edge Detection.
Traditional Approaches and Theory.
Models of Edges.
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.
Source Code for the Marr-Hildreth Edge Detector.
Source Code for the Canny Edge Detector.
Source Code for the Shen-Castan Edge Detector.
Chapter 3 Digital Morphology.
Elements of Digital Morphology—Binary Operations.
Implementing Binary Dilation.
Implementation of Binary Erosion.
Opening and Closing.
MAX—A High-Level Programming Language for Morphology.
The "Hit-and-Miss" Transform.
Identifying Region Boundaries.
Opening and Closing.
Segmentation of Textures.
Size Distribution of Objects.
Chapter 4 Grey-Level Segmentation.
Basics of Grey-Level Segmentation.
Using Edge Pixels.
The Method of Grey-Level Histograms.
Minimum Error Thresholding.
Sample Results From Single Threshold Selection.
The Use of Regional Thresholds.
Chow and Kaneko.
Modeling Illumination Using Edges.
Implementation and Results.
Chapter 5 Texture and Color.
Texture and Segmentation.
A Simple Analysis of Texture in Grey-Level Images.
Results from the GLCM Descriptors.
Speeding Up the Texture Operators.
Edges and Texture.
Energy and Texture.
Surfaces and Texture.
Chapter 6 Thinning.
What Is a Skeleton?
The Medial Axis Transform.
Iterative Morphological Methods.
The Use of Contours.
Treating the Object as a Polygon.
Use of a Force Field.
Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm.
Chapter 7 Image Restoration.
Image Degradations—The RealWorld.
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.
Motion Blur—A Special Case.
The Homomorphic Filter—Illumination.
Frequency Filters in General.
Isolating Illumination Effects.
Chapter 8 Classification.
Objects, Patterns, and Statistics.
Features and Regions.
Training and Testing.
Variation: In-Class and Out-Class.
Minimum Distance Classifiers.
Distances Between Features.
Support Vector Machines.
Merging Multiple Methods.
Merging Type 1 Responses.
Converting Between Response Types.
Merging Type 2 Responses.
Merging Type 3 Responses.
Bagging and Boosting.
Chapter 9 Symbol Recognition.
OCR on Simple Perfect Images.
OCR on Scanned Images—Segmentation.
Isolating Individual Glyphs.
OCR on Fax Images—Printed Characters.
The Use of Edges.
Properties of the Character Outline.
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.
Music Symbol Recognition.
Source Code for Neural Net Recognition System.
Chapter 10 Content-Based Search — Finding Images by Example.
Maintaining Collections of Images.
Features for Query by Example.
Color Image Features.
Color Quad Tree.
Hue and Intensity Histograms.
Results from Simple Color Features.
Other Color-Based Methods.
Grey-Level Image Features.
Edge Density—Boundaries Between Objects.
Boolean Edge Density.
Test of Spatial Sampling.
Objects, Contours, Boundaries.
Chapter 11 High-Performance Computing for Vision and Image Processing.
Paradigms for Multiple-Processor Computation.
The Message-Passing Interface System.
Running MPI Programs.
Real Image Computations.
Using a Computer Network—Cluster Computing.
A Shared Memory System—Using the PC Graphics Processor.
Practical Textures in OpenGL.
Shader Programming Basics.
Vertex and Fragment Shaders.
Required GLSL Initializations.
Reading and Converting the Image.
Passing Parameters to Shader Programs.
Putting It All Together.
Speedup Using the GPU.
Developing and Testing Shader Code.
Finding the Needed Software.
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