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OpenCV with Python By Example

You're reading from   OpenCV with Python By Example Build real-world computer vision applications and develop cool demos using OpenCV for Python

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781785283932
Length 296 pages
Edition 1st Edition
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Author (1):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (14) Chapters Close

Preface 1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Creating a Panoramic Image 7. Seam Carving 8. Detecting Shapes and Segmenting an Image 9. Object Tracking 10. Object Recognition 11. Stereo Vision and 3D Reconstruction 12. Augmented Reality Index

What are integral images?


If we want to compute Haar features, we will have to compute the summations of many different rectangular regions within the image. If we want to effectively build the feature set, we need to compute these summations at multiple scales. This is a very expensive process! If we want to build a real time system, we cannot spend so many cycles in computing these sums. So we use something called integral images.

To compute the sum of any rectangle in the image, we don't need to go through all the elements in that rectangular area. Let's say AP indicates the sum of all the elements in the rectangle formed by the top left point and the point P in the image as the two diagonally opposite corners. So now, if we want to compute the area of the rectangle ABCD, we can use the following formula:

Area of the rectangle ABCD = AC – (AB + AD - AA)

Why do we care about this particular formula? As we discussed earlier, extracting Haar features includes computing the areas of a large...

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