Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

From derivatives to edges


This chapter covers both image derivatives and edge detection. So far, we are done with the first half of the chapter, that is, image derivatives. The remainder of the chapter will be based on edge detection algorithms. Before we embark on an explanation of the various edge detection algorithms out there and the nuances of implementing them using OpenCV/C++, let's take a moment to get a feeling for how these two topics are related. This would not only help you appreciate why these two topics have been put together in the same chapter, but will also make the transition from derivatives to edge detection seamless.

For a moment, let's forget about computer vision or OpenCV and think as a layman. Now if I ask you, what do you understand by edges in images, what would your answer be? Well, to put it simply, we refer to the boundaries of objects as edges. Most of the natural images that one might expect to come across would consist of a finite number of objects (some of...