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)

Learning from Examples

Nowadays, machine learning is very often used to solve difficult machine vision problems. In fact, it is a rich field of research encompassing many important concepts that would deserve an entire cookbook by themselves. This chapter surveys some of the main machine learning techniques and explains how these can be deployed in computer vision systems using OpenCV.

At the core of machine learning is the development of computer systems that can learn, by themselves, how to react to data inputs. Instead of being explicitly programmed, machine learning systems automatically adapt and evolve when examples of desired behaviors are presented to them. Once a successful training phase is completed, it is expected that the trained system will output the correct response to new unseen queries.

Machine learning can solve many types of problems; however, our focus here...