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

Describing and matching local intensity patterns


The SURF and SIFT keypoint detection algorithms, discussed in Chapter 8 , Detecting Interest Points, define a location, an orientation, and a scale for each of the detected features. The scale factor information is useful for defining the size of a window of analysis around each feature point. Thus, the defined neighborhood would include the same visual information no matter at what scale of the object to which the feature belongs has been pictured. This recipe will show you how to describe an interest point's neighborhood using feature descriptors. In image analysis, the visual information included in this neighborhood can be used to characterize each feature point in order to make each point distinguishable from the others. Feature descriptors are usually N-dimensional vectors that describe a feature point in a way that is invariant to change in lighting and to small perspective deformations. Generally, descriptors can be compared using...