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

Laplacian - yet another edge detection technique


In this section, we are going to look at yet another technique that is available for edge detection: Laplacian. Simply put, Laplacian is nothing but the second derivative of the image, where the second derivative refers to the derivative of the derivative. Mathematically, this is represented as follows:

When we were looking for edges using the first derivative, we saw that regions that are potentially edge regions have a sufficiently high magnitude of the derivative (gradient). As it turns out, in the same edge regions, the second derivative is zero. This phenomenon is used as a criterion to detect edges using the Laplacian operator.

The Laplacian() function in OpenCV implements the Laplacian operator that we just discussed. In fact, a single call to Laplacian() will handle both the dimensions, x and y. Internally, it calls the Sobel() function to calculate gradients. A code snippet showing the implementation of Laplacian() is as follows:

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