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  • Book Overview & Buying Mastering OpenCV 3
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Mastering OpenCV 3

Mastering OpenCV 3 - Second Edition

By : Jason Saragih
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Mastering OpenCV 3

Mastering OpenCV 3

By: Jason Saragih
Buy this Book

Overview of this book

As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You’ll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3.
Table of Contents (7 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
Cartoonifier and Skin Changer for Raspberry Pi
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Cartoonifier and Skin Changer for Raspberry Pi
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Accessing the webcam
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Main camera processing loop for a desktop app
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Implementation of the skin color changer
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Summary
2
Exploring Structure from Motion Using OpenCV
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Exploring Structure from Motion Using OpenCV
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Structure from Motion concepts
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Estimating the camera motion from a pair of images
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Reconstructing the scene
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Reconstruction from many views
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Refinement of the reconstruction
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Using the example code
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Summary
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References
3
Number Plate Recognition using SVM and Neural Network
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Number Plate Recognition using SVM and Neural Network
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Introduction to ANPR
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ANPR algorithm
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Plate detection
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Plate recognition
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Summary
4
Non-Rigid Face Tracking
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Non-Rigid Face Tracking
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Overview
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Utilities
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Geometrical constraints
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Facial feature detectors
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Face detection and initialization
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Face tracking
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Summary
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References
5
3D Head Pose Estimation Using AAM and POSIT
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3D Head Pose Estimation Using AAM and POSIT
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Active Appearance Models overview
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Active Shape Models
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Model Instantiation - playing with the AAM
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AAM search and fitting
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POSIT
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Summary
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References
6
Face Recognition Using Eigenfaces or Fisherfaces
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Face Recognition Using Eigenfaces or Fisherfaces
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Introduction to face recognition and face detection
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Summary
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References

Introduction to face recognition and face detection

Face recognition is the process of putting a label to a known face. Just like humans learn to recognize their family, friends, and celebrities just by seeing their face, there are many techniques for a computer to learn to recognize a known face. These generally involve four main steps:

  1. Face detection: This is the process of locating a face region in an image (a large rectangle near the center of the following screenshot). This step does not care who the person is, just that it is a human face.
  2. Face preprocessing: This is the process of adjusting the face image to look more clear and similar to other faces (a small grayscale face in the top-center of the following screenshot).
  3. Collecting and learning faces: This is the process of saving many preprocessed faces (for each person that should be recognized), and then learning how to recognize them.
  4. Face recognition: This is the process that checks which of the collected people are most similar...
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83
Tech Concepts
36
Programming languages
73
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Mastering OpenCV 3
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