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OpenCV By Example

You're reading from   OpenCV By Example Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781785280948
Length 296 pages
Edition 1st Edition
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Authors (3):
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Vinícius G. Mendonça Vinícius G. Mendonça
Author Profile Icon Vinícius G. Mendonça
Vinícius G. Mendonça
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with OpenCV 2. An Introduction to the Basics of OpenCV FREE CHAPTER 3. Learning the Graphical User Interface and Basic Filtering 4. Delving into Histograms and Filters 5. Automated Optical Inspection, Object Segmentation, and Detection 6. Learning Object Classification 7. Detecting Face Parts and Overlaying Masks 8. Video Surveillance, Background Modeling, and Morphological Operations 9. Learning Object Tracking 10. Developing Segmentation Algorithms for Text Recognition 11. Text Recognition with Tesseract Index

Chapter 6. Learning Object Classification

In the previous chapter, we introduced you to the basic concepts of object segmentation and detection. This means isolating the objects that appear in an image for future processing and analysis.

This chapter covers how to classify each of these isolated objects. In order to allow us to classify each object, we need to train our system to be capable of learning the required parameters to decide which specific label should be assigned to the detected object (depending on the different categories taken into account during the training phase).

This chapter is going to introduce you to the basic concepts of machine learning to classify images with different labels.

We will create a basic application based on the segmentation algorithm, as discussed in Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection. This segmentation algorithm extracts parts of an image, which contains objects. For each object, we will extract the...

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