8. Advanced Topics | OpenCV Essentials

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Table Of Contents

OpenCV Essentials

By : Suarez

4.3 (13)

OpenCV Essentials

4.3 (13)

By: Suarez

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started

1. Getting Started

Setting up OpenCV

API concepts and basic datatypes

Our first program – reading and writing images and videos

Reading and playing a video file

Live input from a camera

Summary

2. Something We Look At – Graphical User Interfaces

2. Something We Look At – Graphical User Interfaces

Using OpenCV's highgui module

Text and drawing

Selecting regions

Using Qt-based functions

Summary

3. First Things First – Image Processing

3. First Things First – Image Processing

Pixel-level access and common operations

Image histogram

Histogram equalization

Brightness and contrast modeling

Histogram matching and LUT

Conversion from RGB to other color spaces

Filtering with the retina model

Arithmetic and geometrical transforms

Summary

What else?

4. What's in the Image? Segmentation

4. What's in the Image? Segmentation

Thresholding

Contours and connected components

Flood fill

Watershed segmentation

GrabCut

Summary

What else?

5. Focusing on the Interesting 2D Features

5. Focusing on the Interesting 2D Features

Interest points

Feature detectors

Feature descriptor extractors

Descriptor matchers

Summary

What else?

6. Where's Wally? Object Detection

6. Where's Wally? Object Detection

Object detection

Detecting objects with OpenCV

Cascades are beautiful

Latent SVM

Scene text detection

Summary

What else?

7. What Is He Doing? Motion

7. What Is He Doing? Motion

Motion history

Reading video sequences

The Lucas-Kanade optical flow

The Gunnar-Farneback optical flow

The Mean-Shift tracker

The CamShift tracker

The Motion templates

The Background subtraction technique

Image alignment

Summary

What else?

8. Advanced Topics

8. Advanced Topics

Machine learning

The KNN classifier

The Random Forest classifier

SVM for classification

What about GPUs?

Setting up OpenCV with CUDA

Our first GPU-based program

Going real time

Summary

What else?

Index

Index

Machine learning

Machine learning deals with techniques that allow computers to learn and make decisions by themselves. A central concept in machine learning is the classifier. A classifier learns from the examples in a dataset, where the label of each sample is known. Usually, we have two datasets at hand: training and test. The classifier builds a model using the training set. This trained classifier is expected to predict the label of new unseen samples, so we finally use the test set to validate it and assess label recognition rates.

In this section, we explain the different classes and functions that OpenCV provides for classification, and simple examples of their use. Machine learning classes and functions for statistical classification, regression, and clustering of data are all included in the ml module.

Visually different images

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Programming languages

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