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 noise and edge detection


We are almost at the end of our chapter on edge detection. Before we close off the topic, I want to stress a practical aspect concerning edge detection algorithms. While talking about image filtering in Chapter 2, Image Filtering we discussed image noise. We said that noise in an image is not that uncommon and can occur due to a variety of factors. In this section, we're going to look at image noise with reference to edge detection.

So far, we have learned that edge detection algorithms rely on detecting abrupt changes in pixel intensity values. Now try to think of the effect that noise has on the pixel values in an image. It can do two things:

  1. It can change the intensity of the group of pixels in an otherwise uniform area of the original image in such a manner that it becomes considerably different from its surroundings, thereby getting classified as an edge.

  2. It can alter the intensity values of the pixels that actually belong to edges or regions near the edges...