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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 FREE CHAPTER 2. An Introduction to the Basics of OpenCV 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

Automatic object inspection classification example


Continuing with the example of the previous chapter, the automatic object inspection segmentation, where a carrier tape contains three different types of objects (nuts, screws, and rings), and with Computer Vision, we will be able to recognize each one of them to send notifications to a robot or similar to put each one in different boxes.

In Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection, we preprocessed the input images and extracted the regions of interest of images and isolated each object using different techniques. Now, we will apply all these concepts, as explained in previous sections, in this example to extract features and classify each object and allow to possible robot to put each one in different boxes. In our application, we are only going to show the labels of each image in an image, but we can send the positions in the image and the labels to other devices as a robot.

Then, our goal is from an input...

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