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Mastering OpenCV 3

Mastering OpenCV 3 - Second Edition

By : Saragih
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Mastering OpenCV 3

Mastering OpenCV 3

By: 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

Utilities

Before diving into the intricacies of face tracking, a number of book-keeping tasks and conventions common to all face-tracking methods must first be introduced. The rest of this section will deal with these issues. An interested reader may want to skip this section at the first reading and go straight to the section on geometrical constraints.

Object-oriented design

As with face detection and recognition, programmatically, face tracking consists of two components: data and algorithms. The algorithms typically perform some kind of operation on the incoming (that is, online) data by referencing prestored (that is, offline) data as a guide. As such, an object-oriented design that couples algorithms with the data they rely on is a convenient design choice.

In OpenCV v2.x, a convenient XML/YAML file storage class was introduced that greatly simplifies the task of organizing offline data for use in the algorithms. To leverage this feature, all classes described in this chapter will implement...

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83
Tech Concepts
36
Programming languages
73
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Mastering OpenCV 3
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