Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By : Robert Laganiere
Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By: Robert Laganiere

Overview of this book

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
Table of Contents (21 chapters)
OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Finding objects and faces with a cascade of Haar features


We learned in the previous recipe, some of the basic concepts of machine learning. We showed how a classifier can be built by collecting samples of the different classes of interest. However, for the approach that was considered in this previous recipe, training a classifier simply consists of storing all the samples' representations. From there, the label of any new instance can be predicted by looking at the closest (nearest neighbor) labeled point. For most machine learning methods, training is rather an iterative process during which machinery is built by looping over the samples. Performance of the classifier thus produced gradually improves as more samples are presented. Learning eventually stops when a certain performance criterion is reached or when no more improvements can be obtained by considering the current training dataset. This recipe will present a machine learning algorithm that follows this procedure, the cascade...