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OpenCV 3.x with Python By Example

You're reading from   OpenCV 3.x with Python By Example Make the most of OpenCV and Python to build applications for object recognition and augmented reality

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788396905
Length 268 pages
Edition 2nd Edition
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Authors (2):
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Gabriel Garrido Calvo Gabriel Garrido Calvo
Author Profile Icon Gabriel Garrido Calvo
Gabriel Garrido Calvo
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Contributors
Packt Upsell
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. Seam Carving 7. Detecting Shapes and Segmenting an Image 8. Object Tracking 9. Object Recognition 10. Augmented Reality 11. Machine Learning by an Artificial Neural Network 1. Other Books You May Enjoy

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:

The following is the code for...

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