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

Introduction to the local binary pattern


The local binary pattern is easy, intuitive, and quite simple to compute. These are some very rare qualities of a feature descriptor! Let's first see what local binary pattern means and how it is computed (on paper, we will come to the implementation soon). After we are done explaining the LBP operator using code samples, we will also try to develop an intuition regarding the type of information that it captures from images. Such an understanding would be crucial when you want to decide whether to use LBP features for a particular problem or not.

At the most basic level, the LBP operator assigns a number between 0 to 255 (inclusive) to every pixel in the input image. After this assignment is made, we construct a histogram out of the values having 256 bins-one for each possible value.

First, let's get into the details of how a number is assigned to every pixel. For a given pixel in the input image, we consider a 3 x 3 neighborhood of that pixel location...