zhongyan liang, sanyuan zhang under review for journal of zhejiang university science c (computers...
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Zhongyan Liang, Sanyuan ZhangUnder 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 {[email protected]}
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Problem setting
Goal: License Plate(LP) tilt correction algorithm robust under various angles.
LP localization problem is considered to be solved
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Main algorithm scheme
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PreprocessingLow-pass Wiener filter
222 /,, yxIyxW
- local mean 22
- variance
- average of all estimated variances for each pixel in the neighborhood
yxI , - pixel intensity
Applied filter size: 3x3
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BinarizationSauvola’s threshold
11,,R
skyxyxT
- local mean
s - standard deviation
R = 128 (for grayscale image)
k – takes values from [0.2,0.5]
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Find fitting points1. 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
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Fitted lines verification
dr
dr
B
T
BT
BT
0
T - angle of the top fitted line
B - angle of the bottom fitted line
Lines are considered to be parallel if
Tn
iTiT r
Tnr
1
1
Bn
iBiB r
Bnr
1
1
- average distance from bounding boxes to the top line
- average distance from bounding boxes to the bottom line
Bir - distance from i th bounding boxes to the bottom line
Tir - distance from i th bounding boxes to the top line
BnTn, - number of fitted points in top and bottom lines
Rotation angle of LP:
2/BT
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Feature extractionUsed if the previous algorithm failed to estimate angle1. 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.
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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
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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
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Experimental resultsData 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 & PCA Fitting One Line Stage I only Stage II only Proposed
Set 1 36.99% 85.62% 88.36% 60.27% 92.47%
Set 2 40.00% 98.18% 98.18% 65.45% 98.18%
03.0tan kAccuracy
kstdDev tan
Robustness
Harris & PCA Fitting One Line Stage I only Stage II only Proposed
Set 1 0.07597 0.02781 0.02833 0.02892 0.02441
Set 2 0.13714 0.00675 0.0071 0.02531 0.0071
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Experimental resultsData 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.
Total Real correct Reported correct Confidence of credibility
Set 1 146 134 129 92.27%
Set 2 56 55 55 100%
ResultsCorrectionCorrectReported
ResultsCorrectionCorrectRealycredibilitofConfidence
Credibility
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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.
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Wiener filter example
Original image Filtered image
Difference Difference with enhanced contrast
Magnified parts
Original Filtered
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Binarization
Radius 5, k=0.2 Radius 5, k=0.5
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Otsu methodIn 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:
ttwttwtw222
211
2
1
01
t
i
iptw
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 level1. Set up initial and 2. Step through all possible thresholds t=0…maximum intensity
- Update and - Compute
3. Desired threshold corresponds to the maximum
iw i
iw i tw2
tw2
255
2t
iptw
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OLS vs PCA