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Python Image Processing Cookbook

You're reading from   Python Image Processing Cookbook Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789537147
Length 438 pages
Edition 1st Edition
Languages
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Author (1):
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Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
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Toc

Table of Contents (11) Chapters Close

Preface 1. Image Manipulation and Transformation 2. Image Enhancement FREE CHAPTER 3. Image Restoration 4. Binary Image Processing 5. Image Registration 6. Image Segmentation 7. Image Classification 8. Object Detection in Images 9. Face Recognition, Image Captioning, and More 10. Other Books You May Enjoy

Human skin segmentation with the GMM-EM algorithm

In this recipe, you will learn how to use a parametric model (namely, a Gaussian mixture model) to detect color and segment the pixels corresponding to human skin in an image. You will be given a dataset containing a set of RGB pixel values and their labels (whether they correspond to human skin or not). This dataset is from the UCI Machine Learning Repository, and it is collected by randomly sampling R, G, and B values from images of the faces of different age groups (young, middle-aged, old), regions, and genders. The following table shows the size of the samples in the dataset to be used:

Total learning sample size

Skin sample size

Non-skin sample size

245057

50859

194198

We shall use the YCbCr colorspace instead of RGB, since it separates the luminance from chrominances in RGB values using a linear transform...
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