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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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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 scale-invariant features


The view invariance of feature detection was presented as an important concept in the introduction of this chapter. While orientation invariance, which is the ability to detect the same points even if an image is rotated, has been relatively well handled by the simple feature point detectors that have been presented so far, the invariance to scale changes is more difficult to achieve. To address this problem, the concept of scale-invariant features has been introduced in computer vision. The idea here is to not only have a consistent detection of keypoints no matter at which scale an object is pictured, but to also have a scale factor associated with each of the detected feature points. Ideally, for the same object point featured at two different scales on two different images, the ratio of the two computed scale factors should correspond to the ratio of their respective scales. In recent years, several scale-invariant features have been proposed, and this...

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