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Index
Title Page
Copyright and Credits
Hands-On Algorithms for Computer Vision
Dedication
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PacktPub.com
Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introduction to Computer Vision
Technical requirements
Understanding computer vision
Learning all about images
Color spaces
Input, process, and output
Computer vision frameworks and libraries
Summary
Questions
Getting Started with OpenCV
Technical requirements
Introduction to OpenCV
The Main modules in OpenCV
Downloading and building/installing OpenCV
Using OpenCV with C++ or Python
Understanding the Mat class
Constructing a Mat object
Deleting a Mat object
Accessing pixels
Reading and writing images
Reading and writing videos
Accessing cameras
Accessing RTSP and network feeds
Mat-like classes
Summary
Questions
Further reading
Array and Matrix Operations
Technical requirements
Operations contained in the Mat class
Cloning a matrix
Calculating the cross-product
Extracting a diagonal
Calculating the dot product
Learning about the identity matrix
Matrix inversion
Element-wise matrix multiplication
The ones and zeroes matrix
Transposing a matrix
Reshaping a Mat object
Element-wise matrix operations
Basic operations
The addition operation
Weighted addition
The subtraction operation
The multiplication and division operations
Bitwise logical operations
The comparison operations
The mathematical operations
Matrix and array-wise operations
Making borders for extrapolation
Flipping (mirroring) and rotating images
Working with channels
Mathematical functions
Matrix inversion
Mean and sum of elements
Discrete Fourier transformation
Generating random numbers
The search and locate functions
Locating non-zero elements
Locating minimum and maximum elements
Lookup table transformation
Summary
Questions
Drawing, Filtering, and Transformation
Technical requirements
Drawing on images
Printing text on images
Drawing shapes
Filtering images
Blurring/smoothening filters
Morphological filters
Derivative-based filters
Arbitrary filtering
Transforming images
Thresholding algorithms
Color space and type conversion
Geometric transformation
Applying colormaps
Summary
Questions
Back-Projection and Histograms
Technical requirements
Understanding histograms
Displaying histograms
Back-projection of histograms
Learning more about back-projections
Comparing histograms
Equalizing histograms
Summary
Questions
Further reading
Video Analysis – Motion Detection and Tracking
Technical requirements
Processing videos
Understanding the Mean Shift algorithm
Using the Continuously Adaptive Mean (CAM) Shift
Using the Kalman filter for tracking and noise reduction
How to extract the background/foreground
An example of background segmentation
Summary
Questions
Object Detection – Features and Descriptors
Technical requirements
Template matching for object detection
Detecting corners and edges
Learning the Harris corner-detection algorithm
Edge-detection algorithms
Contour calculation and analysis
Detecting, descripting, and matching features
Summary
Questions
Machine Learning in Computer Vision
Technical requirements
Support vector machines
Classifying images using SVM and HOG
Training models with artificial neural networks
The cascading classification algorithm
Object detection using cascade classifiers
Training cascade classifiers
Creating samples
Creating the classifier
Using deep learning models
Summary
Questions
Assessments
Chapter 1, Introduction to Computer Vision
Chapter 2, Getting Started with OpenCV
Chapter 3, Array and Matrix Operations
Chapter 4, Drawing, Filtering, and Transformation
Chapter 5, Back-Projection and Histograms
Chapter 6, Video Analysis – Motion Detection and Tracking
Chapter 7, Object Detection – Features and Descriptors
Chapter 8, Machine Learning in Computer Vision
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