Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection

David Millan Escriva,Prateek Joshi,Vinicius G. Mendonca,Roy Shilkrot

Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection
Format
Paperback
Publisher
Packt Publishing Limited
Country
United Kingdom
Published
26 March 2019
Pages
538
ISBN
9781838644673

Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection

David Millan Escriva,Prateek Joshi,Vinicius G. Mendonca,Roy Shilkrot

This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms

Key Features

Discover best practices for engineering and maintaining OpenCV projects Explore important deep learning tools for image classification Understand basic image matrix formats and filters

Book DescriptionOpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.

This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you’ll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you’ll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:

Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millan Escriva Learn OpenCV 4 By Building Projects - Second Edition by David Millan Escriva, Vinicius G. Mendonca, and Prateek Joshi

What you will learn

Stay up-to-date with algorithmic design approaches for complex computer vision tasks Work with OpenCV’s most up-to-date API through various projects Understand 3D scene reconstruction and Structure from Motion (SfM) Study camera calibration and overlay augmented reality (AR) using the ArUco module Create CMake scripts to compile your C++ application Explore segmentation and feature extraction techniques Remove backgrounds from static scenes to identify moving objects for surveillance Work with new OpenCV functions to detect and recognize text with Tesseract

Who this book is forIf you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path.

This item is not currently in-stock. It can be ordered online and is expected to ship in 7-14 days

Our stock data is updated periodically, and availability may change throughout the day for in-demand items. Please call the relevant shop for the most current stock information. Prices are subject to change without notice.

Sign in or become a Readings Member to add this title to a wishlist.