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  • Book Overview & Buying OpenCV Essentials
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OpenCV Essentials

OpenCV Essentials

By : Suarez
4.3 (13)
Buy this Book
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OpenCV Essentials

OpenCV Essentials

4.3 (13)
By: Suarez
Buy this Book

Overview of this book

This book is intended for C++ developers who want to learn how to implement the main techniques of OpenCV and get started with it quickly. Working experience with computer vision / image processing is expected.
Table of Contents (10 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
1. Getting Started
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1. Getting Started
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Setting up OpenCV
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API concepts and basic datatypes
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Our first program – reading and writing images and videos
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Reading and playing a video file
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Live input from a camera
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Summary
2
2. Something We Look At – Graphical User Interfaces
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2. Something We Look At – Graphical User Interfaces
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Using OpenCV's highgui module
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Text and drawing
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Selecting regions
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Using Qt-based functions
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Summary
3
3. First Things First – Image Processing
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3. First Things First – Image Processing
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Pixel-level access and common operations
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Image histogram
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Histogram equalization
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Brightness and contrast modeling
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Histogram matching and LUT
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Conversion from RGB to other color spaces
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Filtering with the retina model
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Arithmetic and geometrical transforms
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Summary
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What else?
4
4. What's in the Image? Segmentation
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4. What's in the Image? Segmentation
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Thresholding
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Contours and connected components
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Flood fill
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Watershed segmentation
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GrabCut
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Summary
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What else?
5
5. Focusing on the Interesting 2D Features
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5. Focusing on the Interesting 2D Features
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Interest points
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Feature detectors
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Feature descriptor extractors
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Descriptor matchers
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Summary
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What else?
6
6. Where's Wally? Object Detection
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6. Where's Wally? Object Detection
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Object detection
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Detecting objects with OpenCV
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Cascades are beautiful
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Latent SVM
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Scene text detection
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Summary
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What else?
7
7. What Is He Doing? Motion
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7. What Is He Doing? Motion
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Motion history
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Reading video sequences
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The Lucas-Kanade optical flow
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The Gunnar-Farneback optical flow
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The Mean-Shift tracker
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The CamShift tracker
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The Motion templates
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The Background subtraction technique
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Image alignment
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Summary
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What else?
8
8. Advanced Topics
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8. Advanced Topics
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Machine learning
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The KNN classifier
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The Random Forest classifier
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SVM for classification
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What about GPUs?
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Setting up OpenCV with CUDA
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Our first GPU-based program
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Going real time
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Summary
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What else?
9
Index
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Index

Chapter 7. What Is He Doing? Motion

In this chapter, we will show you different techniques related to motion, as estimated from video frames. After a short introduction and definitions, we will show you how to read video frames captured from a camera. Then, we will tackle the all-important Optical Flow technique. In the third section, we will show you different functions that can be used for tracking. The Motion history and Background subtraction techniques are explained in the fourth and fifth sections, respectively. Finally, image alignment with the ECC method is explained. Every example has been developed and tested for the latest version of OpenCV in GitHub. Most of the functions can work in the previous versions equally, leading to some changes that will be discussed. Most of the functions introduced in this chapter are in the video module.

Note

To test the latest source code available in GitHub, go to https://github.com/itseez/opencv and download the library code as a ZIP...

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