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

What is machine learning


Most programs that we have worked with as part of this book followed a specific pattern. We gave the computer detailed instructions on how to operate on input data (mostly images in our case), and our algorithm diligently followed our instructions to generate the output (which could have been an image or a sequence of values as in the case of a histogram). For example, let's consider image filtering covered in Chapter 2, Image Filtering. We call the OpenCV function, filter2D(), with the appropriate parameters. The implementation of filter2D() holds the detailed instructions to perform image filtering, that is, it consists of the logic to traverse through all the pixels in the image and perform the correlation operation at each pixel location. Everything, including the input image and the filter parameters, is provided to the algorithm. It simply follows the instructions and generates the output image.

In fact, this is how programs were written for a long time. The...