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Computer Vision with OpenCV 3 and Qt5

You're reading from   Computer Vision with OpenCV 3 and Qt5 Build visually appealing, multithreaded, cross-platform computer vision applications

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788472395
Length 486 pages
Edition 1st Edition
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Author (1):
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Amin Ahmadi Tazehkandi Amin Ahmadi Tazehkandi
Author Profile Icon Amin Ahmadi Tazehkandi
Amin Ahmadi Tazehkandi
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Table of Contents (14) Chapters Close

Preface 1. Introduction to OpenCV and Qt 2. Creating Our First Qt and OpenCV Project FREE CHAPTER 3. Creating a Comprehensive Qt+OpenCV Project 4. Mat and QImage 5. The Graphics View Framework 6. Image Processing in OpenCV 7. Features and Descriptors 8. Multithreading 9. Video Analysis 10. Debugging and Testing 11. Linking and Deployment 12. Qt Quick Applications 13. Other Books You May Enjoy

Understanding histograms


As was mentioned in the introductory part of this chapter, there are a concepts in computer vision that are especially important when dealing with video processing and the algorithms we'll talk about later on in this chapter. One of those concepts is histograms. Since understanding histograms is essential to understanding most of the video analysis topics, we'll go through quite a bit of information about them in this section, before moving on to the next topics. A histogram is often referred to as a way of representing the distribution of data. It is a very simple and complete description, but let's also describe what it means in terms of computer vision. In computer vision, a histogram is a graphical representation of the distribution of pixel values in an image. For example, in a grayscale image, a histogram will be a graph representing the number of pixels that contain each possible intensity in the grayscale (a value between 0 and 255). In an RGB color image...

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