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资源说明：Mastering OpenCV 3 - Second Edition by Daniel Lélis Baggio
English | 4 May 2017 | ASIN: B01N7G0BKE | 250 Pages | AZW3 | 4.82 MB
Key Features
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
Each chapter is a tutorial for an entire project from start to finish, showing you how to apply OpenCV to solve complete problems
Book Description
As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision.
This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You’ll learn how to make AI that can remember and use neural networks to help your applications learn.
By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3.
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
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