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

The Sobel derivative filter


We have just implemented and applied our own derivative filter to an image. Since computing the derivative is quite a fundamental operation in computer vision and image processing, we typically do not want to burden the programmer with the task of having to populate the filter all by himself (as we just did). It is no surprise that OpenCV provides you with a function that can return the final output of the image derivative operation. Also, by varying the parameters of the function call, you can compute both the x and y derivatives. The function's name is Sobel(), named after Irwin Sobel, who came up with the design of this filter along with Gary Feldman in 1968.

What is so special about this filter? Well, the design is slightly different from what we implemented just now. For example, the x-derivative filter is shown as follows:

If you look carefully, you will notice a subtle difference in the Sobel filter. The middle row has been multiplied by two. What this...