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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

By : Robert Laganiere
3.5 (2)
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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

3.5 (2)
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 (15 chapters)
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Detecting a planar target in images


In the previous recipe, we explained how homographies can be used to stitch together images separated by a pure rotation to create a panorama. In this recipe, we also learned that different images of a plane also generate homographies between views. We will now see how we can make use of this fact to recognize a planar object in an image.

How to do it...

Suppose you want to detect the occurrence of a planar object in an image. This object could be a poster, painting, logo, signage, and so on. Based on what we have learned in this chapter, the strategy would consist of detecting feature points on this planar object and to try to match them with the feature points in the image. These matches would then be validated using a robust matching scheme similar to the one we used previously, but this time based on a homography. If the number of valid matches is high, then this must mean that our planar object is visible in the current image.

In this recipe, our mission...

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