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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

Features from Accelerated Segment Test (FAST)


Even though SURF is faster than SIFT, it's just not fast enough for a real-time system, especially when there are resource constraints. When you are building a real-time application on a mobile device, you won't have the luxury of using SURF to do computations in real time. We need something that's really fast and computationally inexpensive. Hence, Rosten and Drummond came up with FAST. As the name indicates, it's really fast!

Instead of going through all the expensive calculations, they came up with a high-speed test to quickly determine if the current point is a potential keypoint. We need to note that FAST is just for keypoint detection. Once keypoints are detected, we need to use SIFT or SURF to compute the descriptors. Consider the following image:

If we run the FAST keypoint detector on this image, you will see something like this:

If we clean it up and suppress the unimportant keypoints, it will look like this:

Following is the code for this...

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