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 (11 chapters)

Neighborhood of a pixel


We have seen image processing operations where the value of a pixel at the output is dependent only on the value of the corresponding pixel at the input. By corresponding, we mean pixels at the same locations (row and column) in the input and output image. Such transformations were represented in mathematical form as follows:

s = T(r)

Here, s and r are the intensity values of a pixel in the output and input respectively. Since we are always dealing with pixels at the same locations, there is no mention of pixel coordinates in the preceding formula. That is to say, the grayscale value of the pixel at the 40th row and 30th column in the output depends on the grayscale value of the pixel at the same coordinates (the 40th row and 30th column) at the input.

This section will introduce you to a slightly more advanced form of image transformation. In the operations that we'll discuss now, the output value at a particular pixel (x, y) is not only dependent on the intensity...