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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

Face recognition is a computer vision success story, despite the complications arising from various skin colors, orientations, facial expressions, hair colors, and lighting conditions. In this chapter, we learned the techniques for facial detection. For each of these techniques, you need to remember that facial detection requires a lot of trained images. Face detection is being widely used in many video surveillance applications, and standard APIs are available for both cloud-based and edge devices from Google, Amazon, Microsoft, and Intel, among others. We will learn about the cloud-based API in Chapter 11, Deep Learning on Edge Devices with CPU/GPU Optimization, and techniques for CNN in Chapter 4, Deep Learning on Images, and Chapter 5, Neural Network Architecture and Models. In this chapter, the CNN model for face detection and expression classification was briefly...

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