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

Image filters


If you carefully observe the example that we discussed in the previous section, you will notice that during the process of computing the output intensity at (x, y), we basically multiplied the intensity values of all the 3 x 3 neighbors by 1/9 and added them all up. Let's create a small matrix of dimensions 3 x 3 (the size of the neighborhood under consideration) and fill all the cells with the value 1/9 as shown in the following image:

We'll call this a filter or a kernel. Now, we'll make use of this filter to calculate the output intensity value corresponding to any arbitrary input pixel (x, y), say the pixel having an intensity value of 6 (see the following image). How do we go about doing that? Well, we place the filter over the image in such a manner that the central grid in the filter lies right on top of the pixel at position (x, y)-(2, 2) in our case (I have assumed 1-based indexing for both rows and columns). Once we place the filter in this manner, it will completely...