1. Zhongyan Liang, Sanyuan Zhang Under review for Journal of Zhejiang University Science C (Computers & Electronics) Publisher: Springer A Credible Tilt License Plate Correction Method Based on Pairwise Parallel Lines Andy {andrey.korea@gmail.com}andrey.korea@gmail.com

2. 2 Intelligent Systems Lab. Problem setting Goal: License Plate(LP) tilt correction algorithm robust under various angles. LP localization problem is considered to be solved

3. 3 Intelligent Systems Lab. Main algorithm scheme

4. 4 Intelligent Systems Lab. Preprocessing Low-pass Wiener filter - local mean - variance - average of all estimated variances for each pixel in the neighborhood - pixel intensity Applied filter size: 3x3

5. 5 Intelligent Systems Lab. Binarization Sauvola’s threshold - local mean - standard deviation R = 128 (for grayscale image) k – takes values from [0.2,0.5]

6. 6 Intelligent Systems Lab. Find fitting points 1.Find bounding boxes of connected pixels 2.Count bounding boxes heights distribution for the following intervals: [0.05H, 0.2H]; [0.15H, 0.3H]; [0.25H, 0.4H]; [0.35H, 0.5H]; [0.45H, 0.6H];[0.55H, 0.7H]; [0.65H, 0.8H]; [0.75H, H] Where H is the height of LP 3.Select the interval with the maximum value as the candidate interval 4.Use bounding boxes of selected interval to draw upper and lower lines by selecting highest and lowest points of bounding box. 5.Find fitting lines using Least Squares method

7. 7 Intelligent Systems Lab. Fitted lines verification - angle of the top fitted line - angle of the bottom fitted line Lines are considered to be parallel if - average distance from bounding boxes to the top line - average distance from bounding boxes to the bottom line - distance from i th bounding boxes to the bottom line - distance from i th bounding boxes to the top line - number of fitted points in top and bottom lines Rotation angle of LP:

8. 8 Intelligent Systems Lab. Feature extraction Used if the previous algorithm failed to estimate angle 1.Find vertical edges using Sobel edge detector. 2.Use Otsu method for binarization. 3.Remove objects with height of bounding box less than 8 pixels. 4.Use foreground points of binarized Sobel image as feature points. 5.Use Principle Component Analysis (PCA) to find best fitting line.

9. 9 Intelligent Systems Lab. Experimental results (a)Original image (b)The lines fitted and failure by using the method based on parallel lines (c)The vertical edges (d)Correction results

10. 10 Intelligent Systems Lab. Comparison with other methods (a) Original Image (b) Method by using Harris Feature and PCA (c) Method by using One Fitted Straight Line (d) Method by using Vertical Edges and PCA (Stage II only) (e) Method by using Two Fitted Straight Lines (Stage I only) (f) The proposed method

11. 11 Intelligent Systems Lab. Experimental results Data set 1: tilt license plates (k >= 0.03) in a variety of environments. Data set 2: non-tilt license plates (k < 0.03) in the case of sufficient sunshine. Harris & PCAFitting One LineStage I onlyStage II onlyProposed Set 1 36.99%85.62%88.36%60.27%92.47% Set 2 40.00%98.18% 65.45%98.18% Accuracy Robustness Harris & PCAFitting One LineStage I onlyStage II onlyProposed Set 1 0.075970.027810.028330.028920.02441 Set 2 0.137140.006750.00710.025310.0071

12. 12 Intelligent Systems Lab. Experimental results Data set 1: tilt license plates (k >= 0.03) in a variety of environments. Data set 2: non-tilt license plates (k < 0.03) in the case of sufficient sunshine. TotalReal correctReported correctConfidence of credibility Set 1 14613412992.27% Set 2 5655 100% Credibility

13. 13 Intelligent Systems Lab. Conclusions Advantages : Combined method for LP tilt correction proposed Result verification allows to use additional correction algorithm if needed Experiments shown promising results Disadvantages: Goal setting is uncertain. Input images and initial conditions not described. What error level is acceptable for further recognition? Number of experiments is insufficient to prove the effectiveness of algorithm. Not enough analysis and discussions.

14. 14 Intelligent Systems Lab. Wiener filter example Original imageFiltered image DifferenceDifference with enhanced contrast Magnified parts OriginalFiltered

15. 15 Intelligent Systems Lab. Binarization Radius 5, k=0.2Radius 5, k=0.5

16. 16 Intelligent Systems Lab. Otsu method In Otsu's method we exhaustively search for the threshold that minimizes the intra-class variance, defined as a weighted sum of variances of the two classes: Where Algorithm N.Otsu, “A Threshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man and Cybernetics, vol.9, issue 1, pp.62 – 66, Jan. 1979 Compute histogram and probabilities of each intensity level 1.Set up initial and 2.Step through all possible thresholds t=0…maximum intensity -Update and -Compute 3.Desired threshold corresponds to the maximum

17. 17 Intelligent Systems Lab. OLS vs PCA