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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

Introduction to ANPR


Automatic Number Plate Recognition (ANPR), also known as Automatic License-Plate Recognition (ALPR), Automatic Vehicle Identification (AVI), or Car Plate Recognition (CPR), is a surveillance method that uses Optical Character Recognition (OCR) and other methods such as segmentations and detection to read vehicle registration plates.

The best results in an ANPR system can be obtained with an infrared (IR) camera, because the segmentation steps for detection and OCR segmentation are easy, clean, and minimize errors. This is due to the laws of light, the basic one being that the angle of incidence equals the angle of reflection; we can see this basic reflection when we see a smooth surface such as a plane mirror. Reflection off of rough surfaces such as paper leads to a type of reflection known as diffuse or scatter reflection. The majority of number plates have a special characteristic named retro-reflection—the surface of the plate is made with a material that is covered...

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