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Speaker: Ching-Hao Lai( 賴璟皓 )

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( 賴璟皓 )

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  1. 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) NTIT IMD Date: 2004/10/6

  2. 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 NTIT IMD

  3. Outline • Introduction • Overview of the proposed system • Experimental Results • Conclusion NTIT IMD

  4. 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. NTIT IMD

  5. 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) NTIT IMD

  6. Overview • Input Image • Vertical Edge Detection • Edge Density Map Generation • Binarization and Dilation • License Plate Location • Output Region NTIT IMD

  7. 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)]| NTIT IMD

  8. 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 NTIT IMD

  9. Vertical Edge Detection(3/3) • Vertical edge detector is better than horizontal edge detector. NTIT IMD

  10. 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 NTIT IMD

  11. Edge Density Map Generation(2/2) NTIT IMD

  12. Binarization(1/3) • Otsu Histogram Threshold: Histogram-derived thresholds NTIT IMD

  13. Binarization(2/3) • :變異數 • :概率 (加權)求最小值 NTIT IMD

  14. Binarization(3/3) NTIT IMD

  15. 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 NTIT IMD

  16. Dilation(2/4) NTIT IMD

  17. 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 NTIT IMD

  18. Dilation(4/4) NTIT IMD

  19. 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 NTIT IMD

  20. License Plate Location(2/2) Blue Block Width=4 Height=6 NTIT IMD

  21. 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 NTIT IMD

  22. 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. NTIT IMD

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