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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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Toc

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

Summary

In this chapter, we learned about the importance of computer vision and the challenges we face in the field of computer vision. We also learned about color spaces, edge detection, and the different types of image transformation, as well as the many examples of using OpenCV. We are going to use a few of these techniques in later chapters. 

We learned about the building blocks of an image and how a computer sees an image. We also learned about the importance of color space techniques, such as convolution. We are going to apply all the techniques that we covered here in future chapters.

In the next chapter, we are going to apply computer-vision techniques and implement a software pipeline for detecting road markings. We will first apply this process to an image and then apply it to a video. In the next chapter, we are going to apply several of the techniques that we covered in this chapter, such as edge detection and Hough transformation.

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