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

Overfitting


This completes our discussion on some representative machine learning algorithms. We will now focus on some extremely crucial issues that we need to keep in mind while we apply these ML algorithms in any application domain. First, we will discuss the concept of overfitting to our training data.

The whole point of presenting our learning algorithm with training data is that it can, in the future, predict labels for data points that it has never seen. The ability of any learning algorithm to apply its learnt set of rules to completely new and unseen data is known as the generalization ability of the algorithm. The aim of training any ML classifier is that it should generalize unseen data well.

Let's briefly go back to an example that we introduced early on in this chapter. When students attend classes, a professor teaches them a concept using some illustrative examples (training data). The students (ML algorithms) are expected to build a mental model out of the information they are...