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

Haar features


This section will describe the kind of features that are extracted from the input images when we are trying to develop a face detection system. Since we have already stated that detecting faces in images is no trivial task for machines, you would expect the feature extraction step to be fairly complicated. Surprisingly, it is not so! As it turns out, extracting features is as simple as taking the sum of pixel values values over arbitrary rectangular sub-regions within images! One of the reasons for the popularity of the Viola-Jones detector is its simplicity. Let us learn a bit more about these features.

The feature-set that we are going to talk about in brief here are known as Haar-like features, or simply Haar features. They are nothing but the difference of the sum of intensity values within rectangular regions. If you look at the figure shown in a while, you will notice that each of the four given arrangements of rectangular regions (A, B, C, and D) have some clear and shaded...