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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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Toc

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 does it work?


We have been talking about image resizing and how we should consider the image's content when we resize it. So, why on earth is it called seam carving? It should just be called content-aware image resizing, right? Well, there are many different terms that are used to describe this process, such as image retargeting, liquid scaling, seam carving, and so on. The reason it's called seam carving is because of the way we resize the image. The algorithm was proposed by Shai Avidan and Ariel Shamir. You can refer to the original paper at http://dl.acm.org/citation.cfm?id=1276390.

We know that the goal is to resize the given image and keep the interesting content intact. So, we do that by finding the paths of least importance in that image. These paths are called seams. Once we find these seams, we remove them from the image to obtain a rescaled image. This process of removing, or "carving", will eventually result in a resized image. This is the reason we call it "seam carving"...

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