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

Retrieving similar images using the histogram comparison


Content-based image retrieval is an important problem in computer vision. It consists of finding a set of images that present content that is similar to a given query image. Since we have learned that histograms constitute an effective way to characterize an image's content, it makes sense to think that they can be used to solve the content-based image retrieval problem.

The key here is to be able to measure the similarity between two images by simply comparing their histograms. A measurement function that will estimate how different, or how similar, two histograms are will need to be defined. Various such measures have been proposed in the past, and OpenCV proposes a few of them in its implementation of the cv::compareHist function.

How to do it...

In order to compare a reference image with a collection of images and find the ones that are the most similar to this query image, we created an ImageComparator class. This class contains...