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

Support vector machines (SVMs) - introduction


Right at the outset of this chapter, we defined the modus operandi of machine learning algorithms. If you recall, we had said that an ML system is presented with training data. It then makes its own set of rules or builds a model, which it uses to further make predictions on unseen (test) data. By revisiting this definition, I want to focus on the two key things that an ML algorithm can do with the training data:

  1. Formulate a set of rules.

  2. Build a model.

We have covered the basics of the k-nearest neighbor classifier in great detail. Let's try to place the operation of the kNN algorithm in the context of the two points we have listed above. Given the training data and a query point to classify, the kNN looks at the neighboring points and decides the class of the query point based on a majority vote. Clearly, this is a case of an ML algorithm that applies a set of rules based on the training data it has been presented with for the purpose of classifying...