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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

Extracting the components' contours


Images generally contain representations of objects. One of the goals of image analysis is to identify and extract these objects. In object detection/recognition applications, the first step is often to produce a binary image that shows you where certain objects of interest could be located. No matter how this binary map is obtained (for example, from the histogram back projection we did in Chapter 4, Counting the Pixels with Histograms, or from motion analysis as we will learn in Chapter 11, Processing Video Sequences), the next step is to extract the objects that are contained in this collection of 1s and 0s.

Consider, for example, the image of buffaloes in a binary form that we manipulated in Chapter 5, Transforming Images with Morphological Operations, as shown in the following figure:

We obtained this image from a simple thresholding operation followed by the application of open and close morphological filters. This recipe will show you how to extract...

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