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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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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

Applying look-up tables to modify the image's appearance

Image histograms capture the way a scene is rendered using the available pixel intensity values. By analyzing the distribution of the pixel values over an image, it is possible to use this information to modify and possibly improve an image. This recipe explains how we can use a simple mapping function, represented by a look-up table, to modify the pixel values of an image. As we will see, look-up tables are often produced from histogram distributions.

How to do it...

A look-up table is a simple one-to-one (or many-to-one) function that defines how pixel values are transformed into new values. It is a 1D array with, in the case of regular gray-level images, 256 entries. Entry i of the table gives you the new intensity value of the corresponding gray level, which is expressed as follows:

    newIntensity= lookup[oldIntensity]; 

  The cv::LUT function in OpenCV applies a look-up table to an image in order to produce a new image...

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