Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Author
1 Digital Image Processing and Analysis
1.1 Introduction
1.2 Image Analysis and Computer Vision Overview
1.3 Digital Imaging Systems
1.4 Image Formation and Sensing
1.4.1 Visible Light Imaging
1.4.2 Imaging Outside the Visible Range of the EM Spectrum
1.4.3 Acoustic Imaging
1.4.4 Electron Imaging
1.4.5 Laser Imaging
1.4.6 Computer-Generated Images
1.5 Image Representation
1.5.1 Binary Images
1.5.2 Gray-Scale Images
1.5.3 Color Images
1.5.4 Multispectral and Multiband Images
1.5.5 Digital Image File Formats
1.6 Key Points
1.7 References and Further Reading
1.8 Exercises
2 Computer Vision Development Tools
2.1 Introduction and Overview
2.2 CVIPtools Windows GUI
2.2.1 Image Viewer
2.2.2 Analysis Window
2.2.3 Utilities Window
2.2.4 Help Window
2.2.5 Development Tools
2.3 CVIPlab for C/C++ Programming
2.3.1 Toolkit, Toolbox Libraries and Memory Management in C/C++
2.3.2 Image Data and File Structures
2.4 The MATLAB CVIP Toolbox
2.4.1 Help Files
2.4.2 M-Files
2.4.3 CVIPtools for MATLAB GUI
2.4.4 CVIPlab for MATLAB
2.4.5 Vectorization
2.4.6 Using CVIPlab for MATLAB
2.4.7 Adding a Function
2.4.8 A Sample Batch Processing M-File
2.4.9 VIPM File Format
2.5 References and Further Reading
2.6 Introductory Programming Exercises
2.7 Computer Vision and Image Analysis Projects
3 Image Analysis and Computer Vision
3.1 Introduction
3.1.1 Overview
3.1.2 System Model
3.2 Preprocessing
3.2.1 Region of Interest Geometry
3.2.2 Arithmetic and Logic Operations
3.2.3 Enhancement with Spatial Filters
3.2.4 Enhancement with Histogram Operations
3.2.5 Image Quantization
3.3 Binary Image Analysis
3.3.1 Thresholding Bimodal Histograms
3.3.2 Connectivity and Labeling
3.3.3 Basic Binary Object Features
3.3.4 Computer Vision: Binary Object Classification
3.4 Key Points
3.5 References and Further Reading
3.6 Exercises
3.6.1 Programming Exercises
3.7 Supplementary Exercises
3.7.1 Supplementary Programming Exercises
4 Edge, Line and Shape Detection
4.1 Introduction and Overview
4.2 Edge Detection
4.2.1 Gradient Operators
4.2.2 Compass Masks
4.2.3 Thresholds, Noise Mitigation and Edge Linking
4.2.4 Advanced Edge Detectors
4.2.5 Edges in Color Images
4.2.6 Edge Detector Performance
4.3 Line Detection
4.3.1 Hough Transform
4.3.2 Postprocessing
4.4 Corner and Shape Detection
4.4.1 Corner Detection
4.4.2 Shape Detection with the Hough Transform
4.5 Key Points
4.6 References and Further Reading
4.7 Exercises
4.7.1 Programming Exercises
4.8 Supplementary Exercises
4.8.1 Supplementary Programming Exercises
5 Segmentation
5.1 Introduction and Overview
5.1.1 Segmentation System Model and Preprocessing
5.1.2 Image Segmentation Categories
5.2 Region Growing and Shrinking
5.3 Clustering Techniques
5.4 Boundary Detection
5.5 Deep Learning Segmentation Methods
5.5.1 Convolution Neural Networks
5.6 Combined Segmentation Approaches
5.7 Morphological Filtering
5.7.1 Erosion, Dilation, Opening, Closing
5.7.2 Hit-or-Miss Transform, Thinning and Skeletonization
5.7.3 Iterative Modification
5.8 Segmentation Evaluation Methods
5.8.1 Binary Object Shape Comparison Metrics
5.8.2 Subjective Methods for Complex Images
5.8.3 Objective Methods for Complex Images
5.9 Key Points
5.10 References and Further Reading
5.11 Exercises
5.11.1 Programming Exercises
5.12 Supplementary Exercises
5.12.1 Supplementary Programming Exercises
6 Feature Extraction and Analysis
6.1 Introduction and Overview
6.1.1 Feature Extraction
6.2 Shape Features
6.3 Histogram Features
6.4 Color Features
6.5 Fourier Transform and Spectral Features
6.6 Texture Features
6.7 Region-Based Features: SIFT/SURF/GIST
6.8 Feature Extraction with CVIPtools
6.9 Feature Analysis
6.9.1 Feature Vectors and Feature Spaces
6.9.2 Distance and Similarity Measures
6.9.3 Data Preprocessing
6.10 Key Points
6.11 References and Further Reading
6.12 Exercises
6.12.1 Programming Exercises
6.13 Supplementary Exercises
6.13.1 Supplementary Programming Exercises
7 Pattern Classification
7.1 Introduction
7.2 Algorithm Development: Training and Testing Methods
7.3 Nearest Neighbor (NN), K-NN, Nearest Centroid, Template Matching
7.4 Bayesian, Support Vector Machines, Random Forest Classifiers
7.5 Neural Networks and Deep Learning
7.6 Cost/Risk Functions and Success Measures
7.7 Pattern Classification Tools: Python, R, MATLAB and CVIPtools
7.7.1 Python
7.7.2 R: Bayesian Modeling and Visualization Tools
7.7.3 MATLAB: Statistics and Machine Learning
7.7.4 CVIPtools
7.8 Key Points
7.9 References and Further Reading
7.10 Exercises
7.10.1 Programming Exercises
7.11 Supplementary Exercises
7.11.1 Supplementary Programming Exercises
8 Application Development Tools
8.1 Introduction and Overview
8.2 CVIP Algorithm Test and Analysis Tool
8.2.1 Overview and Capabilities
8.2.2 How to Use CVIP-ATAT
8.2.2.1 Running CVIP-ATAT
8.2.2.2 Creating a New Project
8.2.2.3 Inserting Images
8.2.2.4 Inputting an Algorithm
8.2.2.5 Executing an Experiment
8.3 CVIP-ATAT: Application Development Necrotic Liver Tissue
8.3.1 Introduction and Overview
8.3.2 The Algorithm
8.3.3 Conclusion
8.4 CVIP-ATAT: Application Development with Fundus Images
8.4.1 Introduction and Overview
8.4.2 The New Algorithm
8.4.3 Conclusion
8.5 CVIP-ATAT: Automatic Mask Creation of Gait Images
8.5.1 Introduction
8.5.2 Gait Analysis Images
8.5.3 Preprocessing
8.5.4 Algorithm Combinations
8.5.5 Results Analysis
8.5.6 Conclusion
References
8.6 CVIP Feature Extraction and Pattern Classification Tool
8.6.1 Overview and Capabilities
8.6.2 How to Use CVIP-FEPC
8.6.2.1 Running CVIP-FEPC
8.6.2.2 Creating a New Project
8.6.2.3 Entering Classes in CVIP-FEPC
8.6.2.4 Adding Images and Associated Classes
8.6.2.5 Applying Feature Extraction and Pattern Classification
8.6.2.6 Running a Single Test with Training and Test Sets
8.6.2.7 The Result File
8.6.2.8 Running a Leave-One-Out Test in Combinatoric Mode
8.7 CVIP-FEPC: Application Development with Thermograms
8.7.1 Introduction and Overview
8.7.2 Setting Up Experiments
8.7.3 Running the Experiments and Analyzing Results
8.7.4 Conclusion
8.8 CVIP-FEPC: Identification of Bone Cancer in Canine Thermograms
8.8.1 Introduction
8.8.2 Clinical Application Development
8.8.2.1 Image Database
8.8.2.2 Feature Extraction and Pattern Classification
8.8.2.3 Experimental Setup
8.8.3 Results and Discussion
8.8.4 Conclusion
References
8.9 MATLAB CVIP Toolbox GUI: Detection of Syrinx in Canines with Chiari Malformation via Thermograms
8.9.1 Introduction
8.9.2 Material and Methods
8.9.2.1 Image Data Acquisition
8.9.2.2 ROI Extraction
8.9.2.3 MATLAB
8.9.2.4 CVIPtools
8.9.3 MATLAB CVIP Toolbox
8.9.3.1 Feature Extraction and Pattern Classification
8.9.3.2 Features
8.9.3.3 Data Normalization Methods
8.9.3.4 Distance Metrics
8.9.3.5 Classification Methods
8.9.4 CVIPtools MATLAB Toolbox GUI
8.9.4.1 Feature Extraction Using the MATLAB GUI
8.9.4.2 Pattern Classification Using MATLAB GUI
8.9.5 Results and Discussion
8.9.6 Conclusion
References
Index