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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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Tools
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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

Backprojecting a histogram to detect specific image content


A histogram is an important characteristic of an image's content. If you look at an image area that shows a particular texture or a particular object, then the histogram of this area can be seen as a function that gives the probability that a given pixel belongs to this specific texture or object. In this recipe, you will learn how the concept of histogram backprojection can be advantageously used to detect specific image content.

How to do it...

Suppose you have an image and you wish to detect specific content inside it (for example, in the following image, the clouds in the sky). The first thing to do is to select a region of interest that contains a sample of what you are looking for. This region is the one inside the rectangle drawn on the following test image:

In our program, the region of interest is obtained as follows:

    cv::Mat imageROI; 
    imageROI= image(cv::Rect(216,33,24,30)); // Cloud region 

You then extract...

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