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Mastering OpenCV 4

You're reading from   Mastering OpenCV 4 A comprehensive guide to building computer vision and image processing applications with C++

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Product type Paperback
Published in Dec 2018
Publisher
ISBN-13 9781789533576
Length 280 pages
Edition 3rd Edition
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Authors (2):
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Roy Shilkrot Roy Shilkrot
Author Profile Icon Roy Shilkrot
Roy Shilkrot
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
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Toc

Table of Contents (12) Chapters Close

Preface 1. Cartoonifier and Skin Color Analysis on the RaspberryPi FREE CHAPTER 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 11. Other Books You May Enjoy

Example comparative performance test of algorithms

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...

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