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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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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

Algorithm options in OpenCV

OpenCV has many algorithms covering the same subject. When implementing a new processing pipeline, sometimes there is more than one choice for a step in the pipeline. For example, in Chapter 2, Explore Structure from Motion with the SfM Module, we made an arbitrary decision to use AKAZE features for finding landmarks between the images to estimate camera motion, and sparse 3D structure, however; there are many more kinds of 2D features available in OpenCV's features2D module. A more sensible mode of operation should have been to select the type of feature algorithm to use based on its performance, with respect to our needs. At the very least, we need to be aware of the different options.

Again, we looked to create a convenient way to see whether there are multiple options for the same task. We created a table where we list specific computer vision...

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