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OpenCV 4 with Python Blueprints

You're reading from   OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801811
Length 366 pages
Edition 2nd Edition
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Authors (4):
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Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Dr. Menua Gevorgyan Dr. Menua Gevorgyan
Author Profile Icon Dr. Menua Gevorgyan
Dr. Menua Gevorgyan
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Arsen Mamikonyan Arsen Mamikonyan
Author Profile Icon Arsen Mamikonyan
Arsen Mamikonyan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Fun with Filters 2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER 3. Finding Objects via Feature Matching and Perspective Transforms 4. 3D Scene Reconstruction Using Structure from Motion 5. Using Computational Photography with OpenCV 6. Tracking Visually Salient Objects 7. Learning to Recognize Traffic Signs 8. Learning to Recognize Facial Emotions 9. Learning to Classify and Localize Objects 10. Learning to Detect and Track Objects 11. Profiling and Accelerating Your Apps 12. Setting Up a Docker Container 13. Other Books You May Enjoy

Listing the tasks performed by the app

The app will analyze each captured frame to perform the following tasks:

  • Feature extraction: We will describe an object of interest with Speeded-Up Robust Features (SURF), which is an algorithm used to find distinctive keypoints in an image that are both scale-invariant and rotation invariant. These keypoints will help us to make sure that we are tracking the right object over multiple frames because the appearance of the object might change from time to time. It is important to find keypoints that do not depend on the viewing distance or viewing angle of the object (hence, the scale and rotation invariance).
  • Feature matching: We will try to establish a correspondence between keypoints using the Fast Library for Approximate Nearest Neighbors (FLANN) to see whether a frame contains keypoints similar to the keypoints from our object of interest...
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