As an example, we will set up a scenario where we are required to align overlapping images, like what is done in panorama or aerial photo stitching. One important feature that we need to measure performance is to have a ground truth, a precise measurement of the true condition that we are trying to recover with our approximation method. Ground truth data can be obtained from datasets made available for researchers to test and compare their algorithms; indeed, many of these datasets exist and computer vision researchers use them all the time. One good resource for finding computer vision datasets is Yet Another Computer Vision Index To Datasets (YACVID), https://riemenschneider.hayko.at/vision/dataset/, which has been actively maintained for the past eight years and contains hundreds of links to datasets. The following is also...
-
Book Overview & Buying
-
Table Of Contents
Mastering OpenCV 4 - Third Edition
By :
Mastering OpenCV 4
By:
Overview of this book
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.
Table of Contents (12 chapters)
Preface
Free Chapter
Cartoonifier and Skin Color Analysis on the RaspberryPi
Explore Structure from Motion with the SfM Module
Face Landmark and Pose with the Face Module
Number Plate Recognition with Deep Convolutional Networks
Face Detection and Recognition with the DNN Module
Introduction to Web Computer Vision with OpenCV.js
Android Camera Calibration and AR Using the ArUco Module
iOS Panoramas with the Stitching Module
Finding the Best OpenCV Algorithm for the Job
Avoiding Common Pitfalls in OpenCV
Other Books You May Enjoy
