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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

Image processing with a Raspberry Pi

Raspberry Pi is a single-board tiny computer without a GPU that can be connected to an external camera and other sensor modules and can be programmed in Python to perform computer vision work such as object detection. Raspberry Pis have built-in Wi-Fi, so they can be connected to the internet seamlessly to receive and transfer data. Because of its tiny shape and powerful computing, the Raspberry Pi is a perfect example of an edge device for IoT and computer vision work. Detailed information on Raspberry Pi can be found at https://www.raspberrypi.org/products/. The following photo shows the complete setup for a Raspberry Pi:

The detailed hardware setup for the Raspberry Pi will be described in the following subsections.

Note that this image will be listed in the following setup section several times.
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