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
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Preface

Recognizing faces using nearest neighbors of local binary patterns


Our first exploration of machine learning techniques will start with what is probably the simplest approach, namely nearest neighbor classification. We will also present the local binary pattern feature, a popular representation encoding the textural patterns and contours of an image in a contrast independent way.

Our illustrative example will concern the face recognition problem. This is a very challenging problem that has been the object of numerous researches over the past 20 years. The basic solution we present here is one of the face recognition methods implemented in OpenCV. You will quickly realize that this solution is not very robust and works only under very favorable conditions. Nevertheless, this approach constitutes an excellent introduction to machine learning and to the face recognition problem.

How to do it...

The OpenCV library proposes a number of face recognition methods implemented as a subclass of the generic...