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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
Languages
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

Scanning an image with pointers

In most image-processing tasks, you need to scan all pixels of the image in order to perform a computation. Considering the large number of pixels that will need to be visited, it is essential that you perform this task in an efficient way. This recipe, and the next one, will show you different ways of implementing efficient scanning loops. This recipe uses the pointer arithmetic.

Getting ready

We will illustrate the image-scanning process by accomplishing a simple task: reducing the number of colors in an image.

Color images are composed of 3-channel pixels. Each of these channels corresponds to the intensity value of one of the three primary colors, red, green, and blue. Since each of these values is an 8-bit unsigned character, the total number of colors is 256x256x256, which is more than 16 million colors. Consequently, to reduce the complexity of an analysis, it is sometimes useful to reduce the number of colors in an image. One way to achieve this goal...

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