Book Image

OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition

By : David Millán Escrivá, Robert Laganiere
Book Image

OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition

By: David Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)

Filtering the Images

Filtering is one of the fundamental tasks in signal and image processing. It is a process aimed at selectively extracting certain aspects of an image that are considered to convey important information in the context of a given application. Filtering removes noise in images, extracts interesting visual features, allows image resampling, and so on. It finds its roots in the general signals and systems theory. We will not cover this theory in detail here. However, this chapter will present some of the important concepts related to filtering and will show you how filters can be used in image-processing applications. But first, let's begin with a brief explanation of the concept of frequency domain analysis.

When we look at an image, we observe how the different gray levels (or colors) are distributed over the image. Images differ from each other because...