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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283840
Length 266 pages
Edition 1st Edition
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Authors (2):
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Joe Minichino Joe Minichino
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Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (16) Chapters Close

Learning OpenCV 3 Computer Vision with Python Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Setting Up OpenCV FREE CHAPTER 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 3 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Detecting and Recognizing Objects 8. Tracking Objects 9. Neural Networks with OpenCV – an Introduction Index

Object segmentation using the Watershed and GrabCut algorithms


Calculating a disparity map can be very useful to detect the foreground of an image, but StereoSGBM is not the only algorithm available to accomplish this, and in fact, StereoSGBM is more about gathering 3D information from 2D pictures, than anything else. GrabCut, however, is a perfect tool for this purpose. The GrabCut algorithm follows a precise sequence of steps:

  1. A rectangle including the subject(s) of the picture is defined.

  2. The area lying outside the rectangle is automatically defined as a background.

  3. The data contained in the background is used as a reference to distinguish background areas from foreground areas within the user-defined rectangle.

  4. A Gaussians Mixture Model (GMM) models the foreground and background, and labels undefined pixels as probable background and foregrounds.

  5. Each pixel in the image is virtually connected to the surrounding pixels through virtual edges, and each edge gets a probability of being foreground...

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