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

Detecting lines in images with the Hough transform


In our human-made world, planar and linear structures abound. As a result, straight lines are frequently visible in images. These are meaningful features that play an important role in object recognition and image understanding. The Hough transform is a classic algorithm that is often used to detect these particular features in images. It was initially developed to detect lines in images and, as we will see, it can also be extended to detect other simple image structures.

Getting ready

With the Hough transform, lines are represented using the following equation:

The ρ parameter is the distance between the line and the image origin (the upper-left corner), and θ is the angle of the perpendicular to the line. In this representation, the lines visible in an image have a θ angle between 0 and π radians, while the ρ radius can have a maximum value that equals the length of the image diagonal. Consider, for example, the following set of lines:

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