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OpenCV Computer Vision Application Programming Cookbook Second Edition

You're reading from   OpenCV Computer Vision Application Programming Cookbook Second Edition Over 50 recipes to help you build computer vision applications in C++ using the OpenCV library

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Product type Paperback
Published in Aug 2014
Publisher Packt
ISBN-13 9781782161486
Length 374 pages
Edition 1st Edition
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Author (1):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
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Toc

Table of Contents (13) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing Color Images with Classes 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Processing Video Sequences Index

Detecting FAST features at multiple scales


FAST has been introduced as a quick way to detect keypoints in an image. With SURF and SIFT, the emphasis was on designing scale-invariant features. More recently, new interest point detectors have been introduced with the objective of achieving both fast detection and invariance to scale changes. This recipe presents the Binary Robust Invariant Scalable Keypoints (BRISK) detector. It is based on the FAST feature detector that we described in a previous recipe of this chapter. Another detector, called ORB (Oriented FAST and Rotated BRIEF), will also be discussed at the end of this recipe. These two feature point detectors constitute an excellent solution when fast and reliable image matching is required. They are especially efficient when they are used in conjunction with their associated binary descriptors, as will be discussed in Chapter 9, Describing and Matching Interest Points.

How to do it...

Following what we did in the previous recipes, the...

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