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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

Common pitfalls and suggested solutions

OpenCV is very feature rich and provides multiple solutions and paths to resolve a visual-understanding problem. With this great power also comes hard work, choosing and crafting the best processing pipeline for the project requirements. Having multiple options means that probably finding the exact best performing solution is next to impossible, as many pieces are interchangeable and testing all the possible options is out of our reach. This problem's exponential complexity is compounded by the input data; more unknown variance in the incoming data will make our algorithm choices even more unstable. In other words, working with OpenCV, or any other computer vision library, is still a matter of experience and art. A priori intuition as to the success of one or another route to a solution is something computer vision engineers develop...

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