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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Pose estimation using DeeperCut and ArtTrack

Human pose estimation is the process of estimating the configuration of the body (pose) from an image or video. It includes landmarks (points), which are similar to joints such as the feet, ankles, chin, shoulder, elbows, hands, head, and so on. We will be doing this automatically using deep learning. If you consider a face, the landmarks are relatively rigid or, rather, relatively constant from face to face, such as the relative position of the eyes to the nose, the mouth to the chin, and so forth.

The following photo provides an example:

Although the body structure remains the same, our bodies aren't rigid. So, we need to detect the different parts of our body relative to the other parts. For example, detecting the feet relative to the knee is very challenging compared to facial detection. Also, we can move our hands and feet...

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