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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

How do we define "interesting"?


Before we start computing the seams, we need to find out what metric we will be using to compute these seams. We need a way to assign "importance" to each pixel so that we can find out the paths that are least important. In computer vision terminology, we say that we need to assign an energy value to each pixel so that we can find the path of minimum energy. Coming up with a good way to assign the energy value is very important because it will affect the quality of the output.

One of the metrics that we can use is the value of the derivative at each point. This is a good indicator of the level of activity in that neighborhood. If there is some activity, then the pixel values will change rapidly. Hence the value of the derivative at that point would be high. On the other hand, if the region were plain and uninteresting, then the pixel values wouldn't change as rapidly. So, the value of the derivative at that point in the grayscale image would be low.

For each...

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