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OpenCV 3 Computer Vision Application Programming Cookbook

You're reading from   OpenCV 3 Computer Vision Application Programming Cookbook Recipes to make your applications see

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Product type Paperback
Published in Feb 2017
Publisher
ISBN-13 9781786469717
Length 474 pages
Edition 3rd Edition
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Author (1):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
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Toc

Table of Contents (15) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing the Colors of an Image 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Reconstructing 3D Scenes 12. Processing Video Sequences 13. Tracking Visual Motion 14. Learning from Examples

Equalizing the image histogram


In the previous recipe, we showed you how the contrast of an image can be improved by stretching a histogram so that it occupies the full range of the available intensity values. This strategy indeed constitutes an easy fix that can effectively improve the quality of an image. However, in many cases, the visual deficiency of an image is not that it uses a too-narrow range of intensities.

Rather, it is that some intensity values are used much more frequently than others. The histogram shown in the first recipe of this chapter is a good example of this phenomenon. The middle-gray intensities are indeed heavily represented, while darker and brighter pixel values are rather rare. One possible way to improve the quality of an image could therefore be to make equal use of all available pixel intensities. This is the idea behind the concept of histogram equalization, that is making the image histogram as flat as possible.

How to do it...

OpenCV offers an easy-to-use...

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