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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

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 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

The Hough transform

The Hough transform is one of the most important topics of computer vision. It is used in feature extraction and image analysis. The Hough transform was invented in 1972 by Richard Duda and Peter Hart, and it was originally called the generalized Hough transform. In general, the technique is used to find instances of objects that are not perfectly within a certain class by means of a voting procedure. 

We can use the Hough transform along with region of interest masking. We will see an example of the detection of road markings in Chapter 5, Finding Road Markings Using OpenCV, using the Hough transform and region of interest masking together.

We will learn about the Hough transform in more detail with the drawing of a 2D coordinate space of x and y inside a straight line, as shown in Fig 4.74.

We know that the equation of a straight line is . The straight line has two parameters, m and c, and we are currently plotting it...

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