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Raspberry Pi Computer Vision Programming

You're reading from   Raspberry Pi Computer Vision Programming Design and implement computer vision applications with Raspberry Pi, OpenCV, and Python 3

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Length 306 pages
Edition 2nd Edition
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Author (1):
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Ashwin Pajankar Ashwin Pajankar
Author Profile Icon Ashwin Pajankar
Ashwin Pajankar
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Computer Vision and the Raspberry Pi 2. Chapter 2: Preparing the Raspberry Pi for Computer Vision FREE CHAPTER 3. Chapter 3: Introduction to Python Programming 4. Chapter 4: Getting Started with Computer Vision 5. Chapter 5: Basics of Image Processing 6. Chapter 6: Colorspaces, Transformations, and Thresholding 7. Chapter 7: Let's Make Some Noise 8. Chapter 8: High-Pass Filters and Feature Detection 9. Chapter 9: Image Restoration, Segmentation, and Depth Maps 10. Chapter 10: Histograms, Contours, and Morphological Transformations 11. Chapter 11: Real-Life Applications of Computer Vision 12. Chapter 12: Working with Mahotas and Jupyter 13. Chapter 13: Appendix 14. Other Books You May Enjoy

Chapter 7: Let's Make Some Noise

In the previous chapter, we learned and demonstrated the concepts of colorspaces and converting them, mathematical transformations, and thresholding operations.

In this chapter, we will learn and demonstrate the concepts related to noise and filtering. This entire chapter is dedicated to understanding the concept of noise in detail. First, we will learn how to simulate various types of noise pattern in depth. Then, we will learn and demonstrate how to use image kernels and the convolution operation. We will also learn how to use the convolution operation to apply various types of filters. Finally, we will learn the basics of low pass filters and demonstrate how to use them to perform blurring and noise removal operations.

We will also use GPIO for demonstrations. In this chapter, we will cover the following topics:

  • Noise
  • Working with kernels
  • 2D convolution with the Signal Processing module in SciPy
  • Filtering and blurring...
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