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Mastering Computer Vision with TensorFlow 2.x

You're reading from   Mastering Computer Vision with TensorFlow 2.x Build advanced computer vision applications using machine learning and deep learning techniques

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781838827069
Length 430 pages
Edition 1st Edition
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Author (1):
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Krishnendu Kar Krishnendu Kar
Author Profile Icon Krishnendu Kar
Krishnendu Kar
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Computer Vision and Neural Networks
2. Computer Vision and TensorFlow Fundamentals FREE CHAPTER 3. Content Recognition Using Local Binary Patterns 4. Facial Detection Using OpenCV and CNN 5. Deep Learning on Images 6. Section 2: Advanced Concepts of Computer Vision with TensorFlow
7. Neural Network Architecture and Models 8. Visual Search Using Transfer Learning 9. Object Detection Using YOLO 10. Semantic Segmentation and Neural Style Transfer 11. Section 3: Advanced Implementation of Computer Vision with TensorFlow
12. Action Recognition Using Multitask Deep Learning 13. Object Detection Using R-CNN, SSD, and R-FCN 14. Section 4: TensorFlow Implementation at the Edge and on the Cloud
15. Deep Learning on Edge Devices with CPU/GPU Optimization 16. Cloud Computing Platform for Computer Vision 17. Other Books You May Enjoy

Summary

In this chapter, we learned about and implemented three different methods of pose estimation – OpenPose, stacked hourglass, and PostNet. We learned how to predict human key points using OpenCV and TensorFlow. Then, we learned about the detailed theory and TensorFlow implementation of the stacked hourglass method. We showed you how to evaluate human poses in a browser and use a webcam for real time estimation of key points. Human pose estimation was then linked to the action recognition model to demonstrate how the two can be used to improve accuracy. The acceleration-based code showed how TensorFlow 2.0 can be used to load data, train the model, and predict actions.

In the next chapter, we will learn how to implement R-CNN and combine it with other CNN models such as ResNet, Inception, and SSD to improve the prediction, accuracy, and speed of object detection.

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