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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere
5 (1)
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

5 (1)
By: 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)
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Tracking Visual Motion

Video sequences are interesting because they show scenes and objects in motion. The preceding chapter introduced the tools for reading, processing, and saving videos. In this chapter, we will look at different algorithms that track the visible motion in a sequence of images. This visible or apparent motion can be caused by objects that move in different directions and at various speeds, or by the motion of the camera (or a combination of both).

Tracking apparent motion is of utmost importance for many applications. It allows you to follow specific objects while they are moving in order to estimate their speed and determine where they are going. It also permits you to stabilize videos taken from handheld cameras by removing or reducing the amplitude of camera jitters. Motion estimation is also used in video coding to compress a video sequence in order to...

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