Cover image for Hands-On Algorithms for Computer Vision

Hands-On Algorithms for Computer Vision

Author Amin Ahmadi Tazehkandi
Release Date: 2018/07/01
ISBN: 9781789130942
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Book Description

Create powerful, accurate, and real-time Computer Vision applications using a perfect blend of algorithms and filters. Also learn about object tracking and foreground extractions with a variety of new filters and algorithms.

Key Features

  • Filter, transform, and manipulate images using MAT class and OpenCV Framework
  • Explore motion detection and object tracking with filters and algorithms
  • Build object detectors using deep learning and machine learning algorithms

Book Description

An arena that has been positively impacted by the advancements in processing power and performance is the field of computer vision. It's only natural that over time, more and more algorithms are introduced to perform computer vision tasks more efficiently. Hands-On Algorithms for Computer Vision is a starting point for anyone who is interested in the field of computer vision and wants to explore the most practical algorithms used by professional computer vision developers. The book starts with the basics and builds up over the course of the chapters with hands-on examples for each algorithm.

Right from the start, you will learn about the required tools for computer vision development, and how to install and configure them. You'll explore the OpenCV framework and its powerful collection of libraries and functions. Starting from the most simple image modifications, filtering, and transformations, you will gradually build up your knowledge of various algorithms until you are able to perform much more sophisticated tasks, such as real-time object detection using deep learning algorithms.

What you will learn

  • Get to grips with machine learning and artificial intelligence algorithms
  • Read, write, and process images and videos
  • Perform mathematical, matrix, and other types of image data operations
  • Create and use histograms from back-projection images
  • Detect motion, extract foregrounds, and track objects
  • Extract key points with a collection of feature detector algorithms
  • Develop cascade classifiers and use them, and train and test classifiers
  • Employ TensorFlow object detection to detect multiple objects

Who this book is for

Hands-On Algorithms for Computer Vision helps those who want to learn algorithms in Computer Vision to create and customize their applications. This book will also help existing Computer Vision developers customize their applications. A basic understanding of computer vision and programming experience is needed.

Table of Contents

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