Mastering OpenCV 4: A comprehensive guide to building computer vision and image processing applications with C++,3rd Edition
Authors: Roy Shilkrot – David Millan Escriva
ISBN-10: 1789533570
ISBN-13: 9781789533576
Released: 2018-12-27
Print Length 页数: 280 pages
Book Description
Learn
Build real-world computer vision problems with working OpenCV code samples
Uncover best practices in engineering and maintaining OpenCV projects
Explore algorithmic design approaches for complex computer vision tasks
Work with OpenCV’s most updated API (v4.0.0) through projects
Understand 3D scene reconstruction and Structure from Motion (SfM)
Study camera calibration and overlay AR using the ArUco Module
About
Mastering OpenCV,now in its third edition,targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum,the book delivers complete projects from ideation to running code,targeting current hot topics in computer vision such as face recognition,landmark detection and pose estimation,and number recognition with deep convolutional networks.
You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects.
By the end of the book,you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.
Features
Learn about the new features that help unlock the full potential of OpenCV 4
Build face detection applications with a cascade classifier using face landmarks
Create an optical character recognition (OCR) model using deep learning and convolutional neural networks
contents
1 Cartoonifier and Skin Color Analysis on the RaspberryPi
2 Explore Structure from Motion with the SfM Module
3 Face Landmark and Pose with the Face Module
4 Number Plate Recognition with Deep Convolutional Networks
5 Face Detection and Recognition with the DNN Module
6 Introduction to Web Computer Vision with OpenCV.js
7 Android Camera Calibration and AR Using the ArUco Module
8 iOS Panoramas with the Stitching Module
9 Finding the Best OpenCV Algorithm for the Job
10 Avoiding Common Pitfalls in OpenCV
