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Mastering OpenCV with Practical Computer Vision Projects

You're reading from   Mastering OpenCV with Practical Computer Vision Projects This is the definitive advanced tutorial for OpenCV, designed for those with basic C++ skills. The computer vision projects are divided into easily assimilated chapters with an emphasis on practical involvement for an easier learning curve.

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Product type Paperback
Published in Dec 2012
Publisher Packt
ISBN-13 9781849517829
Length 340 pages
Edition 1st Edition
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Table of Contents (15) Chapters Close

Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Cartoonifier and Skin Changer for Android FREE CHAPTER 2. Marker-based Augmented Reality on iPhone or iPad 3. Marker-less Augmented Reality 4. Exploring Structure from Motion Using OpenCV 5. Number Plate Recognition Using SVM and Neural Networks 6. Non-rigid Face Tracking 7. 3D Head Pose Estimation Using AAM and POSIT 8. Face Recognition using Eigenfaces or Fisherfaces Index

ANPR algorithm


Before explaining the ANPR code, we need to define the main steps and tasks in the ANPR algorithm. ANPR is divided in two main steps: plate detection and plate recognition. Plate detection has the purpose of detecting the location of the plate in the whole camera frame. When a plate is detected in an image, the plate segment is passed to the second step—plate recognition—which uses an OCR algorithm to determine the alphanumeric characters on the plate.

In the next figure we can see the two main algorithm steps, plate detection and plate recognition. After these steps the program draws over the camera frame the plate's characters that have been detected. The algorithms can return bad results or even no result:

In each step shown in the previous figure, we will define three additional steps that are commonly used in pattern recognition algorithms:

  1. Segmentation: This step detects and removes each patch/region of interest in the image.

  2. Feature extraction: This step extracts from...

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