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A Fast License Plate Extraction Method on Complex Background. Author: Hongliang Bai, Junmin Zhu and Changping Liu Source: Proceedings of IEEE on Intelligent Transportation Systems, Volume 2, Oct. 12-15, 2003, P.P. 985 - 987. Speaker: Ching-Hao Lai( 賴璟皓 ). Date: 2004/10/6.

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speaker ching hao lai

A Fast License Plate Extraction Method on Complex Background

Author: Hongliang Bai, Junmin Zhu and Changping Liu

Source: Proceedings of IEEE on Intelligent Transportation Systems, Volume 2, Oct. 12-15, 2003,

P.P. 985 - 987

Speaker: Ching-Hao Lai(賴璟皓)

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Date: 2004/10/6

slide2

Extraction and Tracking of the License Plate Using Hough Transform and Voted Block Matching

Author: Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.;

Nakatani, H.; Saji, H.;

Source: Intelligent Vehicles Symposium, 2003.

Proceedings. IEEE , June 9-11, 2003

Pages:243 - 246

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outline
Outline
  • Introduction
  • Overview of the proposed system
  • Experimental Results
  • Conclusion

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introduction 1 2
Introduction(1/2)
  • LPR has turned out to be an important research issue.
  • LPR system consists of three parts:

License plate detection

Character segmentation

Character recognition

  • A fast license plate localization algorithm for monitoring the highway ticketing system.

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introduction 2 2
Introduction(2/2)
  • LP detect method overview:

Morphological operations

Edge extraction

Combination of gradient features

Neural Network for color classification

Vector quantization

Back-propagation neural network (BPNN)

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overview
Overview
  • Input Image
  • Vertical Edge Detection
  • Edge Density Map Generation
  • Binarization and Dilation
  • License Plate Location
  • Output Region

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vertical edge detection 1 3
Vertical Edge Detection(1/3)
  • Horizontal Sobel Filter

g(h)=|[f(i-1,j-1)+2f(i-1,j)+f(i-1,j+1)]

-[f(i+1,j-1)+2f(i+1,j)+f(i+1,j+1)]|

  • VerticalSobel Filter

g(v)=|[f(i-1,j-1)+2f(i,j-1)+f(i+1,j-1)]

-[f(i-1,j+1)+2f(i,j+1)+f(i+1,j+1)]|

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vertical edge detection 2 3
Vertical Edge Detection(2/3)
  • Sobel Filter Horizontal

g(h)=|(30*1+33*2+119*1)

-(36*1+115*2+114*1)|=165

  • Sobel Filter Vertical g(v)=|(30*1+33*2+36*1)

-(119*1+115*2+114*1)|=331

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vertical edge detection 3 3
Vertical Edge Detection(3/3)
  • Vertical edge detector is better than horizontal edge detector.

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edge density map generation 1 2
Edge Density Map Generation(1/2)
  • Density Formulation:
    • 3 X 15 block and center at (I,j)
    • d(I,j) represents the edge density map

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binarization 1 3
Binarization(1/3)
  • Otsu Histogram Threshold:

Histogram-derived thresholds

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binarization 2 3
Binarization(2/3)
  • :變異數
  • :概率 (加權)求最小值

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dilation 1 4
Dilation(1/4)
  • Before dilation, we use a nonlinear filter

remove narrow horizontal lines.

If Bottom-Top<T (Threshold=5) then

For(i=Top;i<=Bottom;i++) p(i)=0

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dilation 3 4
Dilation(3/4)
  • We dilate the image use a horizontal mask.

If Right-Left<T (Threshold=9) then

For(i=Left;I<=Right;i++) p(i)=255

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license plate location 1 2
License Plate Location(1/2)
  • Connected Component Analysis
  • Feature Extraction

Aspect ratio (R) = W / H

Area (A) = W x H

Density (D) = N / ( W x H )

  • Combination of candidate regions by the connected density
  • Getting Final Candidate regions

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license plate location 2 2
License Plate Location(2/2)

Blue Block Width=4 Height=6

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experimental results
Experimental Results
  • Data Source:

478 real scene images acquired from the real highway ticketing station

  • Resolution: 768x534
  • Different Light condition:

cloudy, sunny, daytime, night time

  • Different kind of vehicle:

van, truck, car

  • 459 of 478 (96%) image were successful detect 100ms per image

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conclusion
Conclusion
  • A fast license plate localization scheme is presented in the paper.
  • The most serious shortcoming of our method is in falling to locate the license plate that is badly deficient.
  • It is relatively robust to variations of the lighting conditions and different kinds of vehicle.

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