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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

Plate recognition


The second step in license plate recognition aims to retrieve the characters of the license plate with optical character recognition. For each detected plate, we proceed to segment the plate for each character, and use an Artificial Neural Network (ANN) machine-learning algorithm to recognize the character. Also in this section we will learn how to evaluate a classification algorithm.

OCR segmentation

First, we obtain a plate image patch as the input to the segmentation OCR function with an equalized histogram, we then need to apply a threshold filter and use this threshold image as the input of a Find contours algorithm; we can see this process in the next figure:

This segmentation process is coded as:

Mat img_threshold;
threshold(input, img_threshold, 60, 255, CV_THRESH_BINARY_INV);
if(DEBUG)
  imshow("Threshold plate", img_threshold);
Mat img_contours;
img_threshold.copyTo(img_contours);
//Find contours of possibles characters
vector< vector< Point> > contours...
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