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

Erosion and dilation


The good thing about erosion and dilation is that the fundamental principles that they operate upon are similar to image filtering. So, the basic concepts that you need to understand the nature of their operations are already known to you.

Now, when we talked about image filtering, we had mentioned that image averaging using a filter-based approach is a linear operation--the same type of operation is performed at all pixel locations and the output pixel intensity at any location is a linear combination of the intensities of its neighboring pixels. When we talk of erosion and dilation, we relax the constraints of linear combinations (the condition for the same operation to be performed at all locations still holds). In spite of the overwhelming similarity between filtering and morphological operations, there are some differences between the two. First, the kernel that is used in image thresholding is given the name of structuring element. And a change in the name always...