Vehicle license plate vlp recognition system
This presentation is the property of its rightful owner.
Sponsored Links
1 / 23

Vehicle License Plate (VLP) Recognition System PowerPoint PPT Presentation


  • 102 Views
  • Uploaded on
  • Presentation posted in: General

Vehicle License Plate (VLP) Recognition System. By German H. Flores and Gurpal Bhoot. Agenda. Introduction Goal and Motivation Image Segmentation Feature Extraction Classification Results/Conclusion Future Work. Introduction. Technological advancements in both software and hardware

Download Presentation

Vehicle License Plate (VLP) Recognition System

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Vehicle license plate vlp recognition system

Vehicle License Plate (VLP) Recognition System

By German H. Flores and GurpalBhoot


Agenda

Agenda

  • Introduction

  • Goal and Motivation

  • Image Segmentation

  • Feature Extraction

  • Classification

  • Results/Conclusion

  • Future Work


Introduction

Introduction

  • Technological advancements in both software and hardware

    • Better ways to capture, edit and analyze images

  • Safety and security of pedestrians and people in motorized vehicles

    • The large number of cars on the roads has increased the probability of an accident occurring

  • With a VLP system, the owner of a car can be easily identified and held responsible for their actions


Video

Video


Process flow

Object Recognition Process

Process Flow


Assumptions

Assumptions

  • Ideal lighting Conditions

  • Non-white car

  • License Plate is in the same region

  • License Plates are similar sizes

  • Only California license plates after 1987

  • License Plates must be white with dark characters

  • Upper case letter O and 0 are the same


Image segmentation

Binary Image

Image Segmentation

  • Convert the original image into a binary image

    • Threshold was chosen through testing

Binary Image

Resize Image

  • Shrink the image

    • Cut out the background

    • Leave only part of the image where license plate is most likely to appear


Vehicle license plate vlp recognition system

Image Segmentation

Windowing Method

  • Windowing Method used to find the license plate from the binary image

    • Send a window (m X n) through binary image, pixel by pixel

Resized Binary Image


Vehicle license plate vlp recognition system

Image Segmentation

Windowing Method

  • Find the license plate by number of white pixels

  • Below is the resulting image from applying the Window Method

Final Binary Image


Vehicle license plate vlp recognition system

Image Segmentation

Connected Component Algorithm

  • Used for separating license plate from the image

  • Finds the different objects

    • Finds the license plate by size and shape

Extracted License Plate

  • Then used for separating the letters and numbers

    • Finds each character and extracts them one by one


Vehicle license plate vlp recognition system

Image Segmentation


Feature extraction

Feature Extraction

  • What features are important for a successful pattern classification?

    • Ex: Color, Area, Perimeter, mean, variance

  • Character

    Recognition


Feature extraction1

Feature Extraction

Area

Perimeter

Compressed and Normalized

Simple Compression

And Normalized Corners

Full Compression And

Normalized Corners

Perimeter of Contour


Feature extraction2

Feature Extraction

(http://www.leewardpro.com/articles/licplatefonts/font-penitentiary.html)

Characters that have holes

  • Features:

  • Area

  • Perimeter

  • Perimeter of Contour

  • Number of Corners in simple

  • compressed Image

  • Number of Corners in full

  • compressed Image

  • Distance Image

  • Normalized Character Image

A B D O P Q R 0 6 8 9

Characters that do not have holes

C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7


Feature extraction3

Feature Extraction

A corner can be defined as the intersection of two edges

  • Harris Corner Detection

A new Corner Matching Algorithm Based on Gradient. (Yu, Haliyan.,., RenCuihua., and QiaoXiaoling)


Feature extraction4

Feature Extraction


Feature extraction5

Feature Extraction

  • Compute X and Y derivatives of the grayscale image

    GxGy

  • Compute products of derivatives

  • Define at each pixel (x,y), the matrix

  • Compute the response at each pixel

  • Threshold on Value R

    0s or negative numbers are the corners


Feature extraction6

Feature Extraction

Character Features Extracted

From Image

Character Features

from Database

Correlation

Corr2()


Results

Results


Results1

Results


Results2

Results


Conclusion overview

Conclusion/Overview

A B D O P Q R 0 6 8 9

C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7


Bibliography

Bibliography


  • Login