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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 do we compute the seams?


Now that we have the energy matrix, we are ready to compute the seams. We need to find the path through the image with the least energy. Computing all the possible paths is prohibitively expensive, so we need to find a smarter way to do this. This is where dynamic programming comes into the picture. In fact, seam carving is a direct application of dynamic programming. We need to start with each pixel in the first row and find our way to the last row. In order to find the path of least energy, we compute and store the best paths to each pixel in a table. Once we've construct this table, the path to a particular pixel can be found by backtracking through the rows in that table.

For each pixel in the current row, we calculate the energy of three possible pixel locations in the next row that we can move to, that is, bottom left, bottom, and bottom right. We keep repeating this process until we reach the bottom. Once we reach the bottom, we take the one with the least...

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