Mastering OpenCV 3 - Second Edition
ISBN-13:
9781786467171
ISBN-10:
1786467178
Edition:
2nd Revised edition
Author:
David Millan Escriva, Roy Shilkrot, Daniel Lélis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih
Publication date:
2017
Publisher:
Packt Publishing
Format:
Paperback
250 pages
Category:
AI & Machine Learning
,
Graphics & Design
,
Computer Science
FREE US shipping
Book details
ISBN-13:
9781786467171
ISBN-10:
1786467178
Edition:
2nd Revised edition
Author:
David Millan Escriva, Roy Shilkrot, Daniel Lélis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih
Publication date:
2017
Publisher:
Packt Publishing
Format:
Paperback
250 pages
Category:
AI & Machine Learning
,
Graphics & Design
,
Computer Science
Summary
Mastering OpenCV 3 - Second Edition (ISBN-13: 9781786467171 and ISBN-10: 1786467178), written by authors
David Millan Escriva, Roy Shilkrot, Daniel Lélis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih, was published by Packt Publishing in 2017.
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Description
Practical Computer Vision Projects
About This Book- Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3
- Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications
- Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problems
This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.
What You Will Learn- Execute basic image processing operations and cartoonify an image
- Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text
- Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video
- Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry
- Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks
- Train and predict pattern-recognition algorithms to decide whether an image is a number plate
- Use POSIT for the six degrees of freedom head pose
- Train a face recognition database using deep learning and recognize faces from that database
As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what
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