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

Chapter 4. Image Histograms

We started off with simple grayscale transformations and gradually moved to image filtering, thresholding, and morphological operations. At the most fundamental level, there is something in common among all the image-processing and computer vision algorithms that we have discussed in this book so far. In each of these processes, there was always some form of computation that was being performed at every pixel. The result of the computation at the input stage dictated the output value of a pixel in the output image (we usually termed it the corresponding output pixel). What this essentially meant was that the output of all of these operations was images of the same size (dimensions) as the input image, and there was one-to-one correspondence between the pixel locations in the input and the output images: this correspondence were governed by the nature of the computation that was performed. For example, when we talked about image averaging, we performed the average...