ntit imd 1 speaker: ching-hao lai( 賴璟皓 ) author: hongliang bai, junmin zhu and changping liu...
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Speaker:Speaker: Ching-Hao Lai( Ching-Hao Lai( 賴璟皓賴璟皓 ))
Author:Author: Hongliang Bai, Junmin Zhu and Changping Liu Hongliang Bai, Junmin Zhu and Changping Liu
Source:Source: Proceedings of IEEE on Intelligent Transportation Proceedings of IEEE on Intelligent Transportation Systems, Volume 2, Oct. 12-15, 2003, Systems, Volume 2, Oct. 12-15, 2003,
P.P. 985 - 987 P.P. 985 - 987
A Fast License Plate Extraction A Fast License Plate Extraction Method on Complex BackgroundMethod on Complex Background
Date: 2004/10/6
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Author:Author: Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.; Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.; Nakatani, H.; Saji, H.;Nakatani, H.; Saji, H.;Source:Source: Intelligent Vehicles Symposium, 2003. Intelligent Vehicles Symposium, 2003. Proceedings. IEEE , June 9-11, 2003Proceedings. IEEE , June 9-11, 2003 Pages:243 - 246Pages:243 - 246
Extraction and Tracking of the License Plate Extraction and Tracking of the License Plate Using Hough Transform and Voted Block Using Hough Transform and Voted Block
MatchingMatching
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OutlineOutline IntroductionIntroduction Overview of the proposed systemOverview of the proposed system Experimental ResultsExperimental Results ConclusionConclusion
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LPR has turned out to be an important LPR has turned out to be an important research issue.research issue.
LPR system consists of three parts:LPR system consists of three parts:
License plate detectionLicense plate detection
Character segmentationCharacter segmentation
Character recognitionCharacter recognition A fast license plate localization algorithm A fast license plate localization algorithm
for monitoring the highway ticketing for monitoring the highway ticketing system.system.
Introduction(1/2)Introduction(1/2)
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LP detect method overview:LP detect method overview:
Morphological operationsMorphological operations
Edge extractionEdge extraction
Combination of gradient featuresCombination of gradient features
Neural Network for color classificationNeural Network for color classification
Vector quantizationVector quantization
Back-propagation neural network (BPNN)Back-propagation neural network (BPNN)
Introduction(2/2)Introduction(2/2)
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Input ImageInput Image
Vertical Edge DetectionVertical Edge Detection Edge Density Map GenerationEdge Density Map Generation Binarization and DilationBinarization and Dilation License Plate LocationLicense Plate Location
Output RegionOutput Region
OverviewOverview
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Horizontal Sobel FilterHorizontal Sobel Filter
g(h)=|[f(i-1,j-1)+2f(i-1,j)+f(i-1,j+1)]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)]|-[f(i+1,j-1)+2f(i+1,j)+f(i+1,j+1)]|
VerticalSobel FilterVerticalSobel Filter
g(v)=|[f(i-1,j-1)+2f(i,j-1)+f(i+1,j-1)]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)]|-[f(i-1,j+1)+2f(i,j+1)+f(i+1,j+1)]|
Vertical Edge Detection(1/3)Vertical Edge Detection(1/3)
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Sobel Filter HorizontalSobel Filter Horizontal
g(h)=|(30*1+33*2+119*1)g(h)=|(30*1+33*2+119*1)
-(36*1+115*2+114*1)|=165-(36*1+115*2+114*1)|=165 Sobel Filter Vertical g(v)=|(30*1+33*2+36*Sobel Filter Vertical g(v)=|(30*1+33*2+36*
1)1)
-(119*1+115*2+114*1)|=331-(119*1+115*2+114*1)|=331
Vertical Edge Detection(2/3)Vertical Edge Detection(2/3)
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Vertical edge detector is better than Vertical edge detector is better than horizontal edge detector.horizontal edge detector.
Vertical Edge Detection(3/3)Vertical Edge Detection(3/3)
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Density Formulation: Density Formulation: • 3 X 15 block and center at (I,j)3 X 15 block and center at (I,j)
• d(I,j) represents the edge density d(I,j) represents the edge density mapmap
Edge Density Map Edge Density Map Generation(1/2)Generation(1/2)
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Edge Density Map Generation(2/2)Edge Density Map Generation(2/2)
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Binarization(1/3)Binarization(1/3)
Otsu Histogram Threshold:Otsu Histogram Threshold:
Histogram-derived thresholdsHistogram-derived thresholds
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Binarization(2/3)Binarization(2/3)
:: 變異數變異數
:: 概率 概率 (( 加權加權 )) 求 最小值求 最小值
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Binarization(3/3)Binarization(3/3)
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Dilation(1/4)Dilation(1/4) Before dilation, we use aBefore dilation, we use a nonlinear filter nonlinear filter
remove narrow horizontal lines.remove narrow horizontal lines.
If Bottom-Top<T (Threshold=5) thenIf Bottom-Top<T (Threshold=5) then
For(i=Top;i<=Bottom;i++) p(i)=0For(i=Top;i<=Bottom;i++) p(i)=0
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Dilation(2/4)Dilation(2/4)
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Dilation(3/4)Dilation(3/4) We dilate the image use a We dilate the image use a horizontal horizontal
mask.mask.
If Right-Left<T (Threshold=9) thenIf Right-Left<T (Threshold=9) then
For(i=Left;I<=Right;i++) p(i)=255For(i=Left;I<=Right;i++) p(i)=255
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Dilation(4/4)Dilation(4/4)
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License Plate Location(1/2)License Plate Location(1/2) Connected Component AnalysisConnected Component Analysis Feature ExtractionFeature Extraction
Aspect ratio (R) = W / HAspect ratio (R) = W / H
Area (A) = W x H Area (A) = W x H
Density (D) = N / ( W x H )Density (D) = N / ( W x H ) Combination of candidate regions by the Combination of candidate regions by the
connected densityconnected density
Getting Final Candidate regionsGetting Final Candidate regions
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License Plate Location(2/2)License Plate Location(2/2)Blue Block Width=4 Height=6Blue Block Width=4 Height=6
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Data Source:Data Source:
478 real scene images acquired from the real 478 real scene images acquired from the real highway ticketing stationhighway ticketing station
Resolution: 768x534Resolution: 768x534 Different Light condition:Different Light condition:
cloudy, sunny, daytime, night timecloudy, sunny, daytime, night time Different kind of vehicle:Different kind of vehicle:
van, truck, carvan, truck, car 459 of 478 (96%) image were successful detect 459 of 478 (96%) image were successful detect
100ms per image100ms per image
Experimental ResultsExperimental Results
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ConclusionConclusion A fast license plate localization scheme is A fast license plate localization scheme is
presented in the paper.presented in the paper. The most serious shortcoming of our method is The most serious shortcoming of our method is
in falling to locate the license plate that is in falling to locate the license plate that is badly deficient.badly deficient.
It is relatively robust to variations of the It is relatively robust to variations of the lighting conditions and different kinds of lighting conditions and different kinds of vehicle.vehicle.