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OpenCV Computer Vision Application Programming Cookbook Second Edition

You're reading from   OpenCV Computer Vision Application Programming Cookbook Second Edition Over 50 recipes to help you build computer vision applications in C++ using the OpenCV library

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Product type Paperback
Published in Aug 2014
Publisher Packt
ISBN-13 9781782161486
Length 374 pages
Edition 1st 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 (13) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing Color Images with Classes 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. Processing Video Sequences Index

Applying look-up tables to modify the image 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 defined 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 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. We can add this function...

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