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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from   Applied Deep Learning and Computer Vision for Self-Driving Cars Build autonomous vehicles using deep neural networks and behavior-cloning techniques

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars FREE CHAPTER 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Edge detection and gradient calculation

Edge detection is a very important feature-extraction technique in computer vision that is used in self-driving cars to go beyond convolution, which we discussed in the previous section. In the previous section, we learned how to extract edges within an image. We converted a color image to grayscale or HSV, and later applied convolution to an image to extract features from it. In this section, we will learn about edge detection and gradient calculation.

Edge detection is a computer-vision feature-extraction tool that is used to detect the sharp changes in an image. 

Let's say that we have three pixels. The first pixel is white, which is represented by 255 (as we have already learned in a previous section of this chapter); the next pixel is 0, which represents black; and the third pixel is also 255. So this means that we are going from white to black and then back to white. Edge detection happens when pixels change...

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