Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Revisiting the image classification framework


Right at the outset of Chapter 6, Face Detection Using OpenCV, we had a brief discourse on image classification systems. Let's revisit that once again and put it in the context of what we have learnt about machine learning so far.

For convenience, we reproduce the figure representing a typical image classification framework that we introduced in Chapter 6, Face Detection Using OpenCV:

This schematic is, in fact, incomplete! While this perfectly describes what happens once our algorithm has already seen the training data and created its model, it does not depict what goes on during the training phase. In order to incorporate that information, let's revise the preceding diagram as follows:

As you can see, during the training phase, we provide both the image and the associated label as inputs (assume that we are dealing with a supervised machine learning setup for now). The first step is to extract relevant features from the input image. We have...