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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 back-projection images


Apart from the visual information in a histogram, there is a more important use for it. This is called back-projection of a histogram, which can be used to modify an image using its histogram, or as we'll see later on in this chapter, to locate objects of interest inside an image. Let's break it down further. As we learned in the previous section, a histogram is the distribution of pixel data over the image, so if we somehow modify the resulting histogram and then re-apply it to the source image (as if it was a lookup table for pixel values), the resulting image would be considered the back-projection image. It is important to note that a back-projection image is always a single-channel image in which the value of each pixel is fetched from its corresponding bin in the histogram.

Let's see this as another example. First of all, here is how a back-projection is calculated in OpenCV:

    calcBackProject(&image, 
      1, 
      channels, 
      histogram...
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