CN100414560C - Vehicle plate extracting method based on small wave conversion and Redon transform - Google Patents

Abstract

A vehicle brand picking-up method bases on Wavelet transform and Radon transform commutation. Switch the really color picture to gray-level picture. Calculate the vertical grads of originality picture and bases the result of Wavelet transform of its plane projection curve to wide orientation about the vehicle brand. Take plane Radon transform to the vehicle brand after wide orientation and locate and delete up and down frames of vehicle brand. Take vertical Radon transformation to the vehicle brand deleted the up frame and down frame and locate and delete left frame and right frame of vehicle brand. Consolidate the colors to get one vehicle brand without frame and having identifiable number. It picks up vehicle brand effectively, quickly and precise in common condition and set veracious identify foundation to the word of vehicle brand.

Description

Vehicle license plate extraction method based on wavelet transformation and Lei Deng conversion

Technical field

The invention belongs to the intelligent transport technology field, particularly the vehicle license plate extraction method in the complex background in the license plate recognition technology.

Background technology

Intelligent transportation system is the forward position research topic of present world traffic and transport field; developed country proposes and has carried out a series of projects; its core is at serious day by day transport need and environmental protection pressure; adopt infotech, the communication technology, computer technology, control technology etc. that conventional traffic transportation system is carried out deep transformation; to improve service efficiency, the security of system of system resource; reduce the consumption and the environmental pollution of resource, China is no exception in this regard.License plate recognition technology in the intelligent transportation system is mainly realized the driving vehicle licence plate is discerned automatically, thereby finish automatic charge, unmanned parking management, the traffic control of critical junction and the tracking of vehicles peccancy or the like function, save manpower, fund with this, improved the efficient of traffic administration simultaneously.Nowadays, along with the raising of computing power and the development of image processing techniques, Vehicle License Plate Recognition System reaches its maturity.See document for details: Da-Shan Gao, Jie Zhou, Car license plates detection from complexscene, Signal Processing Proceedings, 5th International Conference, 21-25 Aug.2000 and document: Shyang-Lih Chang, Li-Shien Chen, YunChung Chung, Sci-Wan Chen, Automatic license platerecognition, Intelligent Transportation Systems, IEEE Transactions on, March 2004 is described.

In Vehicle License Plate Recognition System, it is a gordian technique that car plate extracts, and its purpose is exactly to be partitioned into license plate image from the digital picture of a width of cloth complex background, requires to have high recognition and stronger environmental suitability.But because the complicacy of car plate background and the diversity of vehicle license plate characteristic, up to now, the complete general license plate intelligently localization method of neither one still.Most of localization methods are confined to the solution of certain side problem, as the interference of the inclination of licence plate, illumination, The noise etc., from practical application bigger distance are arranged still.Therefore, how existing achievement in research is combined, consider the ability to work of existing equipment simultaneously, make our Vehicle License Plate Recognition System have good performance and recognition speed is the direction of our current research.See document Zhi-Bin Huang for details, Yan-Feng Guo, Classifier fusion-based vehicle license plate detectionalgorithm, Machine Learning and Cybernetics, 2003 International Conference on, 2-5 Nov.2003 and document Yoshimori, S.Mitsukura, Y.Fukumi, M.Akamatsu, N.Khosal, R.License plate detection system in rainydays, Computational Intelligence in Robotics and Automation., 16-20 July 2003.

And (be called for short: the vehicle license plate extraction method Radon conversion) based on wavelet transformation and Lei Deng conversion, might address this problem, because the high-frequency information of license plate area in entire image is obvious and concentrated, adopt wavelet transformation effectively to extract and outstanding these high-frequency informations, but the car plate that be extracted this moment also includes the car plate frame, the existence of car plate frame will strengthen the difficulty that follow-up characters on license plate is cut apart and discerned, for the difficulty that follow-up Character segmentation and identification are handled can drop to minimum to the full extent, step on conversion to adopting the license plate image of being oriented behind the wavelet transformation to carry out thunder, can locate four frames of car plate out, thereby four frame deletions, only stay discernible character zone, make follow-up Character segmentation and identification become relatively easy.

Wherein, so-called small echo refers to the small-sized ripple that is called small echo based on some, has the frequency of variation and limited duration, and wavelet transformation promptly is meant these small-sized ripples and pending signal are carried out some mathematical operations.It is a kind of geometric transformation that thunder is stepped on conversion, judges the position of whether containing straight line and straight line in the just detected image but the result that the thunder of piece image is stepped on conversion is carried out a series of mathematics.Last car plate refers to the number that is used to identify testing vehicle register that each car plate is installed, and production standardization generally hangs on the front end or the rear portion of vehicle.

The method that present normally used car plate extracts has:

1. the color property based on car plate carries out vehicle license plate extraction method.It is to distinguish license plate area and background area by other regional rainbow features that are different from of extracting car plate, thereby extracts car plate.Advantage is the chromatic information that has made full use of image; Shortcoming is that speed is slower, may lose efficacy for the profuse image of those colors.See document Zhu Wei-gang for details; Hou Guo-jiang; Jia Xing; A study of locating vehicle license plate based on colorfeature and mathematical morphology.Signal Processing, 26-30 Aug.2002,

2. use the Hough conversion and carry out the method that car plate extracts.Thereby it is to search the car plate position by the straight line that extracts the car plate frame.Its shortcoming is that speed is slower, and locatees inaccurately, and it is many interference region to occur.See document K.M.Kim for details, B.J.Lee, K.Lyou.The automatic coefficient and Hough transform.Journal of Control, Automatic and System Engineering.3 (5): 511-519,1997,

3. based on the vehicle license plate extraction method of morphological operator.It is to strengthen by the edge feature of operations such as use expansion, corrosion to car plate, and then extracts car plate.Its shortcoming is can find out considerable interference region to the abundant relatively zone of those edge features, thereby influences the effect that car plate extracts.See document M.Shridhar for details, J.W.Miller.Recognition of license plate image, In Processings of International Conference on DocumentAnalysis and Recognition, 17-20,1999,

Above-mentioned three kinds of methods all possessed a common characteristic when car plate extracted: these methods all are at a particular environment, and the high-frequency information that extracts is limited.In case environmental change, it extracts accuracy rate, and bigger fluctuation will take place, and the performance of whole Vehicle License Plate Recognition System is descended.Therefore lack all-environment adaptability, the method robustness is bad.Our said environment refers to the environment that the car plate original image is gathered herein, different environment, such as weather, constantly, vehicle background of living in and vehicle displacement situation or the like, the image of collection sizable difference often occurs at aspects such as resolution and textural characteristics.High-frequency information refers to the local detail feature of image, and the local detail feature of license plate area is more obvious with respect to other zones.

Summary of the invention

Task of the present invention provides a kind of vehicle license plate extraction method based on wavelet transformation and Lei Deng conversion, adopts method of the present invention, can both extract car plate effectively, quickly and accurately in conventional environment, for the accurate identification of characters on license plate lays the foundation.Described conventional environment comprises: fine, rainy day, car plate level, license plate sloped, stationary vehicle, vehicle movement, situations such as daytime, night.

The present invention ground content for convenience of description, at first make a term definition at this:

1. car plate: rectangle number plate that is used to identify testing vehicle register of installing in each car, number plate has four frames up and down, include 7 characters in the frame, unified production standard is arranged, generally hang on the front end or the rear portion of vehicle, the car plate standard of the vehicle of different purposes is different.

2. gray level image: only comprised monochrome information in the image and without any the image of colouring information.

3. gray scale is judged: a kind ofly judge whether the current image that reads belongs to the image processing means of true color image, specially refer to taken vehicle image is judged herein, basis for estimation is for analyzing the structure of vehicle image matrix, if the vehicle image matrix comprises the three primary colours component, then current institute reads to such an extent that vehicle image belongs to true color image, if not, then be gray level image.

4. gradation conversion: a kind of coloured image is converted into the means of gray level image, utilize formula f (i, j)=0.114*I (i, j, 1)+0.587*I (i, j, 2)+0.299*I (i, j, 3) can be the width of cloth greyscale image transitions gray level image, the row-coordinate value of i presentation video wherein, the row coordinate figure of j presentation video, f (i, j) gray-scale value of the pixel of the capable j row of i in the gray level image after the expression conversion, I (i, j, 1), I (i, j, 2) and I (i, j, 3) represent the R of the pixel of the capable j row of i in the coloured image respectively, G, the value of B component.

5. VG (vertical gradient): for the vertical detail information of outstanding former vehicle image, extract the edge, deduct the gray-scale value of previous pixel, obtain the VG (vertical gradient) value with back pixel gray-scale value of each row.Computing formula is g _V(i, j)=| f (i, j+1)-f (i, j) |, the row-coordinate value of i presentation video wherein, the row coordinate figure of j presentation video, f (i, the j) gray-scale value of the pixel of the capable j row of expression i, f (i, the j+1) gray-scale value of the pixel of the capable j+1 row of expression i, g _V(i, j) the corresponding VG (vertical gradient) value of expression.

6. gradient horizontal projection: a kind of gradient of image and gray scale value in the two-dimensional space is transformed into method in the one-dimensional space after by a functional transformation, this functional transformation is $T_H\left(i\right)=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{g}_V(i,j),$ G wherein _V(n represents total columns of VG (vertical gradient) image matrix, T for i, j) the VG (vertical gradient) value of the capable j row of expression i _H(i) be the capable projection value of i.

7. gaussian filtering: a kind of famous smooth filtering method, carry out Filtering Processing by a filter function transfer pair curve, can make curve more level and smooth, specially refer to gradient horizontal projection curve is carried out filtering herein, filter function is $T_H^{\&prime;}\left(i\right)=\frac{1}{k}\{T_H\left(i\right)+\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}[T_H(i-j)h(j,\mathrm{\&sigma;})+T_H(i+j)h(j,\mathrm{\&sigma;})\left]\right\}.$ T ' wherein _H(i) be filtered gradient horizontal projection value, the variation range of i is from 1 to n, and the height of n representative image also is total line number of image array; W has represented the width size of smooth region, gets 8 herein; $h(j,\mathrm{\&sigma;})=\exp \left(\frac{{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="C20051002165100181.png"\; state="\{\{state\}\}">j</figure-callout>}}^2}{2{\mathrm{\&sigma;}}^2}\right)$ Be Gaussian function, $k=2\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}h(j,\mathrm{\&sigma;})+1,$ σ represents the mean square deviation of gray level image, gets 0.05 herein; T _H(i) i gradient horizontal projection value of expression, T _H(i-j) expression (i-j) individual gradient horizontal projection value T _H(i+j) expression (i+j) individual gradient horizontal projection value.

8. wavelet transformation: specially refer to gradient horizontal projection curve is carried out one-dimensional wavelet transform herein, thereby extract the curvilinear characteristic that helps the car plate location from gradient horizontal projection curve.Specifically be transformed to, at first, construct a Gaussian function $g_s\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \left(\frac{{-\mathrm{<figure-callout\; id="2"\; label="x"\; filenames="C20051002165100181.png"\; state="\{\{state\}\}">x</figure-callout>}}^2}{2{\mathrm{\&sigma;}}^2}\right),$ Wherein σ represents the mean square deviation of Gaussian function, gets 0.5 herein; Then, calculate the first order derivative of this Gaussian function, and this first order derivative as wavelet function; At last this wavelet function and gradient horizontal projection curve are carried out the mathematics convolution algorithm, thereby obtain a wavelet transformation curve.

9. crest: a kind of feature of the value on the curve; Curve values at this place is all bigger than previous curve values that is close to and next-door neighbour's a back curve values.

10. trough: a kind of feature of the value on the curve; Curve values at this place is all littler than previous curve values that is close to and next-door neighbour's a back curve values.

11. license plate candidate area: some have the zone of some feature in piece image, these features are, at first, it one be rectangle or parallelogram with certain size size, to such an extent as to this rectangle or parallelogram can not occupy whole vehicle image too greatly, to such an extent as to can not too little naked eyes invisible, generally be rectangle about high 140mm or parallelogram zone about a long 440mm; Secondly, must there be character in the zone in the rectangle; At last, this rectangle is arranged in the middle and lower part of a vehicle image.Car plate in next width of cloth vehicle image of normal condition is a rectangle, but may take vehicle image the time be deformed into parallelogram owing to a variety of causes causes license plate area.

12. horizontal thunder is stepped on conversion: carry out thunder in the horizontal direction and step on conversion, it is a kind of geometric transformation that thunder is stepped on conversion, it can be used for the straight line in the detection image, in certain level angle scope, carry out thunder and step on conversion, utilize transformation results can detect the straight line of horizontal direction in the image then, specially refer to be used for to survey the upper and lower side frame in the license plate image that rough lumber cuts out herein.The definition horizontal direction is 0 degree, then spends the car plate that every 1 degree rough lumber is cut out in the scopes of 5 degree-5 and carries out thunder and step on conversion, will obtain 11 thunders after the conversion and step on transformation curve.

13. vertical thunder is stepped on conversion: carry out thunder in vertical direction and step on conversion, specially refer to survey left and right side frame in the license plate image with it herein, the definition vertical direction is 90 degree, spend in the scopes of 95 degree 85 and the car plate of deleting upper and lower side frame to be carried out thunder step on conversion, obtain 11 thunders after the conversion and step on transformation curve every 1 degree.

14. wire crest: shape is rendered as the crest of a line or narrower in width, specially refers to step on wire crest in the conversion at the level of car plate and vertical thunder herein, and it is corresponding to the straight line in the license plate image.

15. gray scale upset: the conversion to gray-scale map carries out, deduct each gray-scale value with 255 and get final product, deceive and be converted into whitely, be converted into black in vain.

A kind of new vehicle license plate extraction method provided by the invention based on wavelet analysis and Lei Deng conversion, it comprises the following step:

Step 1. is installed on the highway crossing with camera head, and image acquisition is carried out after in vehicle enters image pickup scope in the appropriate location in charge station or parking lot, obtains containing the original image of license plate image;

Resulting vehicle original image carries out the gray scale judgement in the step 2 pair step 1, if taken vehicle original image is the gray-scale map image, does not then handle; If taken vehicle original image is a true color image, then the vehicle original image is carried out gradation conversion, obtain the gray level image that a width of cloth comprises car plate; Concrete grammar for adopt formula f (i, j)=0.114*I (i, j, 1)+0.587*I (i, j, 2)+0.299*I (i, j, 3) changes, the line position of i presentation video wherein, the column position of j presentation video, f (i, j) gray-scale value of the pixel of the capable j row of i in the gray level image after the expression conversion, * be the multiplying symbol, I (i, j, 1), I (i, j, 2) and I (i, j, 3) represent the R of the pixel of the capable j row of i in the coloured image respectively, G, the value of B component;

Resulting gray level image carries out VG (vertical gradient) calculating in the step 3. pair step 2, obtains a vertical shade of gray figure who includes the vehicle gray level image of car plate; Concrete grammar is for adopting formula

g _V(i, j)=| f (i, j+1)-f (i, j) | carry out VG (vertical gradient) and calculate, the row-coordinate value of i presentation video wherein, the row coordinate figure of j presentation video, f (i, j) gray-scale value of the pixel of the capable j row of expression i, f (i, the j+1) gray-scale value of the pixel of the capable j+1 row of expression i, g _V(i, j) the capable j of expression i row the VG (vertical gradient) value;

Resulting vertical shade of gray figure carries out the gradient horizontal projection in the step 4. pair step 3, obtains a coarse gradient horizontal projection curve; The computing formula of gradient horizontal projection is $T_H\left(i\right)=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{g}_V(i,j),$ G wherein _V(n represents total columns of VG (vertical gradient) image matrix, T for i, j) the VG (vertical gradient) value of the capable j row of expression i _H(i) be the capable projection value of i;

Resulting coarse gradient horizontal projection curve carries out gaussian filtering in the step 5 pair step 4, obtains a level and smooth gradient horizontal projection curve; Filtering method is for adopting formula $T_H^{\&prime;}\left(i\right)=\frac{1}{k}\{T_H\left(i\right)+\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}[T_H(i-j)h(j,\mathrm{\&sigma;})+T_H(i+j)h(j,\mathrm{\&sigma;})\left]\right\}$ Carry out filtering; T ' wherein _H(i) be filtered gradient horizontal projection value, the variation range of i is from 1 to n, and n represents the height of vehicle image; W has represented the width size of smooth region, gets 8 herein; $h(j,\mathrm{\&sigma;})=\exp \left(\frac{{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="C20051002165100181.png"\; state="\{\{state\}\}">j</figure-callout>}}^2}{2{\mathrm{\&sigma;}}^2}\right)$ Be Gaussian function, $k=2\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}h(j,\mathrm{\&sigma;})+1,$ σ represents the mean square deviation of gray level image; T _H(i) i gradient horizontal projection value of expression, T _H(i-j) expression (i-j) individual gradient horizontal projection value, T _H(i+j) expression (i+j) individual gradient horizontal projection value;

Resulting level and smooth gradient horizontal projection curve carries out wavelet transformation in the step 6 pair step 5, obtains a wavelet transformation curve; Small wave converting method is: at first, construct a Gaussian function $g_s\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \left(\frac{{-\mathrm{<figure-callout\; id="2"\; label="x"\; filenames="C20051002165100181.png"\; state="\{\{state\}\}">x</figure-callout>}}^2}{{2\mathrm{\&sigma;}}^2}\right),$ Wherein x is the Gaussian function independent variable, and σ represents the mean square deviation of Gaussian function, gets 0.5 herein; Then, the first order derivative of calculating this Gaussian function obtains $w\left(x\right)=\frac{-x}{\sqrt{2\mathrm{\&pi;}}{\mathrm{\&sigma;}}^3}\exp \left(\frac{{-\mathrm{<figure-callout\; id="2"\; label="x"\; filenames="C20051002165100181.png"\; state="\{\{state\}\}">x</figure-callout>}}^2}{{2\mathrm{\&sigma;}}^2}\right),$ This first order derivative w (x) as wavelet function; At last this wavelet function and gradient horizontal projection curve are carried out the mathematics convolution algorithm, thereby obtain a wavelet transformation curve;

Resulting wavelet transformation curve carries out normalized in the step 7. pair step 6, thereby obtains the curve of a curve values between-1 to 0; Concrete grammar is at first, to obtain wavelet transformation curve maximal value max and minimum value min; Then, utilize formula $s^{\&prime;}\left(x\right)=\frac{(s\left(x\right)-\min )}{(\max -\min )}+1$ Carry out normalization, wherein s (x) is the wavelet transformation curve values before the normalization, and x is the coordinate figure of curve, and s ' is the curve values of the wavelet transformation after the normalized (x);

Resulting wavelet transformation curve carries out gradient horizontal projection curved scanning in the step 8. pair step 7, obtains two the less position coordinateses of trough value in the wavelet transformation curve; Concrete grammar is: seek trough from the starting point of curve, note the position coordinates of little trough value simultaneously, so search the terminal point up to curve;

Step 9. utilizes that resulting wave trough position coordinate carries out the computing of car plate coarse positioning in the step 8, obtains the position coordinates of one or two license plate candidate area in including the vehicle image of car plate; Concrete grammar is: the position coordinates of the less trough value of the curve that provides according to step 8 search for this trough of stating next-door neighbour about the position coordinates of two crests, the position coordinates correspondence of left side crest be the horizontal ordinate of the coboundary of car plate in including the vehicle image matrix of car plate, the horizontal ordinate of representing the position, coboundary of car plate herein with top_boundary, the right crest the position coordinates correspondence be the horizontal ordinate of the lower boundary of car plate in including the vehicle image of car plate, represent the horizontal ordinate of the lower boundary position of car plate herein with bot_boundary;

Step 10 is utilized in the step 9 resulting positional information to carry out the car plate rough lumber and is cut processing, obtains one or two license plate candidate area; Concrete grammar is, at first, the definition element is zero matrix mask, and the line number of mask is consistent with the line number and the columns of the vehicle gray level image matrix that includes car plate respectively with columns; Secondly, all be changed to 1 being positioned at top_boundary elements capable and bot_boundary all row in the ranks among the matrix mask, wherein top_boundary represents the abscissa value of the coboundary of car plate in including the vehicle gray level image of car plate, and bot_boundary represents the abscissa value of the lower boundary of car plate in vehicle image; Then, the element with same coordinate value in matrix mask and the vehicle gray level image matrix is multiplied each other, after multiplying each other, the element in the vehicle gray level image matrix in the non-license plate candidate area is zero, and the element value in the license plate candidate area remains unchanged; At last, the definition element is zero matrix I_cut, the line number of matrix I_cut is bot_boundary-top_boundary, columns and vehicle gray level image matrix column number equate, nonzero element in the matrix of consequence of matrix mask and vehicle gray level image matrix multiple is composed to I_cut, and matrix I_cut just represents license plate candidate area;

Resulting license plate candidate area carries out horizontal thunder and steps on conversion in the step 11. pair step 10, obtains 11 thunders and steps on transformation curve; Specifically be treated to: the definition horizontal direction is 0 degree, spends the car plate that every 1 degree rough lumber is cut out in the scopes of 5 degree-5 and carries out thunder and step on conversion, will obtain 11 thunders after the conversion and step on transformation curve;

The width value that step 12 utilizes 11 thunders in the step 11 to step on transformation curve is determined the coordinate figure of upper and lower side frame in license plate image of car plate; Concrete grammar is: resultant 11 thunders are stepped on the width of transformation curve in the comparison step 12, it is exactly that the angle of inclination of car plate is designated as R_hdegree that the thunder of width minimum is stepped on the pairing translation-angle of transformation curve, first crest value that this moment, thunder was stepped on the transformation curve is designated as R_top greater than the coordinate figure of the wire crest of certain value corresponding to the horizontal ordinate of car plate upper side frame in license plate image, thunder step on the transformation curve last crest value greater than the coordinate figure of the wire crest of certain value corresponding to the car plate lower frame in license plate image ordinate and be designated as R_bot;

Step 13 is utilized the license plate sloped angle that obtains in the step 12 and the coordinate information of upper and lower side frame, proofreaies and correct car plate and deletes the upper and lower side frame of car plate; Specifically be treated to: at first, proofread and correct car plate, if the angle of inclination R_hdegree of car plate is non-vanishing, thinks that then car plate tilts, thereby license plate image is rotated-R_hdegree proofreaies and correct car plate; Define an element then and be zero matrix R_hmask, total columns of this matrix and total line number equal total columns and the total line number of matrix I_cut respectively, the element of matrix R_hmask between R_top and R_bot is changed to 1, and the element that has the same coordinate value among matrix R_hmask and the matrix I_cut is multiplied each other obtains a multiplied result matrix R_htbmask; At last, define an element and be zero matrix R_hcut, total line number of this matrix is R_bot-R_top, total columns of total columns and matrix R_htbmask is identical, nonzero element among the matrix R_htbmask is composed to matrix R_hcut, and matrix R_hcut is exactly an image array of having deleted the license plate image correspondence of car plate upper and lower side frame;

The resulting image array of having deleted the car plate upper and lower side frame carries out vertical thunder and steps on conversion in the step 14. pair step 13, obtains 11 thunders and steps on transformation curve; Specifically be treated to: the definition vertical direction is 90 degree, spends in the scopes of 95 degree 85 and the car plate of deleting upper and lower side frame is carried out thunder steps on conversion every 1 degree, obtains 11 thunders after the conversion and steps on transformation curve;

Step 15 utilizes resultant 11 thunders in the step 14 to step on the crest value of transformation curve, determines the left and right side frame of car plate, and concrete grammar is: at first, find out a thunder of width minimum and step on transformation curve; On this curve, search for the wire crest of a crest value then from left to right greater than certain value, the coordinate figure of this crest is exactly that the ordinate of left frame in this license plate image matrix of having deleted the car plate of car plate upper and lower side frame is designated as R_left, turn left from the right side and search for the wire crest of a crest value greater than certain value, the coordinate figure of this crest is exactly that the ordinate of left frame in this license plate image matrix of having deleted the car plate upper and lower side frame is designated as R_right;

Step 16 is utilized the coordinate figure of the left and right side frame of the resulting car plate of having deleted upper and lower side frame in the step 15, the left and right side frame of deletion car plate; Concrete grammar is, at first, defines an element and is zero matrix R_vmask, and its total line number and total columns equate with total line number and the total columns of matrix R_hcut respectively, matrix R_vmask is positioned at the element that R_left row and R_right be listed as is changed to 1; Then the element that has the same coordinate value among matrix R_vmask and the matrix R_hcut is multiplied each other and obtain a matrix of consequence R_vlrmask; Define an element at last and be zero matrix R_vcut, total columns of this matrix is R_right-R_left, total line number of total line number and matrix R_vlrmask equates, nonzero element among the matrix R_vlrmask is composed to R_vcut, and matrix R_vcut has deleted the license plate image matrix of four frames up and down;

Resulting license plate image accurately carries out car plate color normalized in the step 17. pair step 16, but obtains a license plate image with black background and white number that includes identification number; Concrete grammar is at first, to add up white pixel number and black picture element number in the license plate area; Secondly compare black picture element number and white pixel number,, then carry out the gray scale upset if the white pixel number is most;

By above step, we have just extracted license plate image from the original image that contains car plate.

Need to prove:

1. the calculating of the VG (vertical gradient) in the step 3 is because the pixel value in the license plate area is faster and concentrated than the pixel value variation of non-license plate area, and more obviously concentrates on the VG (vertical gradient) aspect.

2. the gradient horizontal projection in the step 4 is because license plate area one is positioned to have in the zonule of quite high gradient density value, so thereby we at first carry out the horizontal candidate region of gradient horizontal projection location car plate.

3. after carrying out wavelet transformation in the step 6, the value of gradient horizontal projection curve will all become negative value, so crest original on the curve has then become trough through behind the wavelet transformation, trough original on the curve has then become crest through behind the wavelet transformation, so crest of the curve before the trough of the curve behind the time search wavelet transformation of search Wave crest and wave trough just is equivalent to search for wavelet transformation, similarly, the crest of the curve behind the search wavelet transformation just is equivalent to search for the trough of the preceding curve of wavelet transformation.In addition, gradient horizontal projection curve before carrying out wavelet transformation has been handled through a gaussian filtering, the curve of this moment is smoother, but Paint Gloss and got rid of that thereby the false Wave crest and wave trough of curves helps follow-up car plate localization process more before some conversion through the gradient horizontal projection curve behind the wavelet transformation, this also is another advantage of advantage that adopts wavelet transformation.

4. not all trough that satisfies the trough condition is all adopted when searching for trough in the step 8, just think trough when only those trough values being satisfied the certain value scope less than certain value and trough width, trough institute less than certain value and the certain value scope that satisfied of trough width be the empirical value of testing gained.In search, only get the trough and the trough value time little trough value of trough value minimum, the trough correspondence of normal conditions lower wave trough value minimum be exactly license plate area, but be not license plate area sometimes owing to the strong jamming in the vehicle image background causes the zone of minimum trough value correspondence, and the zone that becomes the trough correspondence of trough value sub-minimum is only license plate area, but will be introduced in more license plate candidate area in the follow-up car plate cutting process if extract too much wave trough position, strengthen intractability, two troughs getting trough value minimum through experimental summary as can be known are rational.

5. in the step 9 in the computing of car plate coarse positioning, about search and the trough that satisfies condition next-door neighbour, only those crests that satisfy the certain value scope are just thought true crest during two crests, because thereby the little fluctuating on the curve has caused the existence at false wave peak, the certain value scope that true crest satisfied is the empirical value that obtains by experiment.In addition, resulting in the coarse positioning computing is car plate rough position in including the vehicle image of car plate, owing to be the coarse positioning computing of car plate in this step, so the position coordinates of one or two license plate candidate area might occur.

In the step 12 and 15 the wire crest greater than certain value be the empirical value that obtains by experiment.

The present invention at first carries out pre-service to original image, and real color image is converted to gray level image; Next calculates the VG (vertical gradient) of original image and carries out the car plate coarse positioning according to the wavelet transformation result of the horizontal projection curve of VG (vertical gradient), thereby coarse positioning goes out one or two license plate candidate area.The car plate that coarse positioning is gone out carries out horizontal thunder and steps on conversion, thus the upper and lower side frame of location and deletion car plate, simultaneously, if car plate be tilt be added on correction; Then the car plate of having deleted upper and lower side frame is carried out vertical thunder and step on conversion, thus the left and right side frame of location and deletion car plate; At last the license plate area of having deleted four frames is carried out the color normalized, but finally export the license plate image that a width of cloth only includes identification number.The present invention adopts coarse positioning and accurately locatees and combines, in coarse positioning, adopt the way of wavelet transformation, accurately adopting thunder to step on the way of conversion in the location, and accurately carrying out the normalized of brightness and color behind the location, but finally obtained a car plate with no car plate frame that includes identification number of unified background and number color.Adopt vehicle license plate extraction method of the present invention, can in conventional environment, can both extract car plate effectively, quickly and accurately, for the accurate identification of characters on license plate lays the foundation.

Innovation part of the present invention is:

1. utilize wavelet transformation coarse positioning car plate, wavelet transformation has the advantages that to extract the high frequency characteristics in the signal, utilize wavelet transformation to handling through the gradient vertical projection curve behind gaussian filtering, can extract the Wave crest and wave trough on the curve to the full extent and curve had certain smoothing function, thereby coarse positioning goes out license plate area fast and accurately.

2. utilize thunder to step on conversion and accurately locate car plate, because utilize wavelet transformation to go out car plate by coarse positioning, owing to also include car plate frame and some peripheral non-license plate areas in the car plate that the interference coarse positioning of noise and background goes out, also may make the car plate run-off the straight simultaneously for some reason, this will make the difficulty of follow-up Character segmentation and identification strengthen, detect and delete the frame of car plate and correct car plate and make it to be horizontal so utilize thunder to step on conversion, but what finally obtain is a license plate image that only includes identification number.

3. propose car plate coarse positioning and the accurate method that combines of location, so that the speed of raising method, satisfied the needs of Vehicle License Plate Recognition System real-time.At first utilize the vertical projection of the vertical gradient map of original image to extract two license plate candidate areas at the most roughly, and then these candidate regions application thunders are stepped on transform method accurately orient car plate.

Description of drawings

Fig. 1 is the original image synoptic diagram that contains car plate;

Wherein, the windshield of mark 1 expression vehicle, the bonnet of mark 2 expression vehicles, car light is installed in mark 3 expressions, hang car plate and and the zone of bumper bar, mark 4 expression car plates, mark 5 expression wheels are in the represented car plate of mark 4, capitalization A, B, C, D, E, F and G represent first of car plate respectively, second, the 3rd, the 4th, the 5th, the 6th and the 7th number, wherein the spacing of second number and the 3rd number is slightly larger.

Fig. 2 is that the present invention is through the gradient vertical projection curve behind the gaussian filtering;

Wherein, the value of gradient vertical projection curve after numeric representation process gaussian filtering on the ordinate and the normalization, the numeric representation process gaussian filtering on the horizontal ordinate and the coordinate figure of the gradient vertical projection curve after the normalization.

Fig. 3 is the gradient vertical projection curve of the present invention through wavelet transformation

Wherein, the value of gradient vertical projection curve after numeric representation process wavelet transformation on the ordinate and the normalization, the numeric representation process wavelet transformation on the horizontal ordinate and the coordinate figure of the gradient vertical projection curve after the normalization.

Fig. 4 is the license plate image synoptic diagram that the present invention finally obtains;

Wherein, A, B, C, D, E, F and G represent first of car plate respectively, second, the 3rd, the 4th, the 5th, the 6th and the 7th number.

Fig. 5 is a schematic flow sheet of the present invention

Embodiment:

Adopt method of the present invention, at first in the porch of highway, charge station and other any correct positions adopt the original image of camera head automatic shooting vehicle; Secondly the vehicle original image that photographs is input in the software with the Matlab language compilation as former data and handles automatically, need not any manual intervention therebetween; But finally obtain the license plate image that a width of cloth includes identification number.Adopt 300 colored vehicle images of taking as former data altogether on the spot, coarse positioning goes out 295, and locating accuracy is 98.33%, accurately orients 285, and locating accuracy is 95%, only needs 40ms but locate the license plate image that a width of cloth includes identification number.

In sum, but utilize method of the present invention from the vehicle original image that is provided, to orient the license plate image that includes identification number fast and accurately.

Claims (1)

1. vehicle license plate extraction method based on wavelet analysis and Lei Deng conversion is characterized in that it comprises the following step:

Step 1. is installed on the highway crossing with camera head, and image acquisition is carried out after in vehicle enters image pickup scope in the appropriate location in charge station or parking lot, obtains containing the original image of license plate image;

Resulting vehicle original image carries out the gray scale judgement in the step 2 pair step 1, if taken vehicle original image is the gray-scale map image, does not then handle; If taken vehicle original image is a true color image, then the vehicle original image is carried out gradation conversion, obtain the gray level image that a width of cloth comprises car plate; Concrete grammar for adopt formula f (i, j)=0.114*I (i, j, 1)+0.587*I (i, j, 2)+0.299*I (i, j, 3) changes, the line position of i presentation video wherein, the column position of j presentation video, f (i, j) gray-scale value of the pixel of the capable j row of i in the gray level image after the expression conversion, * be the multiplying symbol, I (i, j, 1), I (i, j, 2) and I (i, j, 3) represent the R of the pixel of the capable j row of i in the coloured image respectively, G, the value of B component;

Resulting gray level image carries out VG (vertical gradient) calculating in the step 3. pair step 2, obtains a vertical shade of gray figure who includes the vehicle gray level image of car plate; Concrete grammar is for adopting formula g _V(i, j)=| f (i, j+1)-f (i, j) | carry out VG (vertical gradient) and calculate, the row-coordinate value of i presentation video wherein, the row coordinate figure of j presentation video, f (i, j) gray-scale value of the pixel of the capable j row of expression i, f (i, the j+1) gray-scale value of the pixel of the capable j+1 row of expression i, g _V(i, j) the capable j of expression i row the VG (vertical gradient) value;

Resulting vertical shade of gray figure carries out the gradient horizontal projection in the step 4. pair step 3, obtains a coarse gradient horizontal projection curve; The computing formula of gradient horizontal projection is $T_H\left(i\right)=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{g}_V(i,j),$ G wherein _V(n represents total columns of VG (vertical gradient) image matrix, T for i, j) the VG (vertical gradient) value of the capable j row of expression i _H(i) be the capable projection value of i;

Resulting coarse gradient horizontal projection curve carries out gaussian filtering in the step 5 pair step 4, obtains a level and smooth gradient horizontal projection curve; Filtering method is for adopting formula $T_H^{\&prime;}\left(i\right)=\frac{1}{k}\{T_H\left(i\right)+\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}[T_H(i-j)h(j,\mathrm{\&sigma;})+T_H(i+j)h(j,\mathrm{\&sigma;})\left]\right\}$ Carry out filtering; T ' wherein _H(i) be filtered gradient horizontal projection value, the variation range of i is from 1 to n, and n represents the height of vehicle image; W has represented the width size of smooth region, gets 8 herein; $h(j,\mathrm{\&sigma;})=\exp \left(\frac{-j^2}{2{\mathrm{\&sigma;}}^2}\right)$ Be Gaussian function,

$k=2\underset{j=1}{\overset{w}{\mathrm{\&Sigma;}}}h(j,\mathrm{\&sigma;})+1,$ σ represents the mean square deviation of gray level image; T _H(i) i gradient horizontal projection value of expression,

T _H(i-j) expression (i-j) individual gradient horizontal projection value, T _H(i+j) expression (i+j) individual gradient horizontal projection value;

Resulting level and smooth gradient horizontal projection curve carries out wavelet transformation in the step 6 pair step 5, obtains a wavelet transformation curve; Small wave converting method is: at first, construct a Gaussian function $g_s\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}\mathrm{\&sigma;}}\exp \left(\frac{-x^2}{2{\mathrm{\&sigma;}}^2}\right),$ Wherein x is the Gaussian function independent variable, and σ represents the mean square deviation of Gaussian function, gets 0.5 herein; Then, the first order derivative of calculating this Gaussian function obtains $w\left(x\right)=\frac{-x}{\sqrt{2\mathrm{\&pi;}}{\mathrm{\&sigma;}}^3}\exp \left(\frac{-x^2}{2{\mathrm{\&sigma;}}^2}\right),$ This first order derivative w (x) as wavelet function; At last this wavelet function and gradient horizontal projection curve are carried out the mathematics convolution algorithm, thereby obtain a wavelet transformation curve;

Resulting wavelet transformation curve carries out normalized in the step 7. pair step 6, thereby obtains the curve of a curve values between-1 to 0; Concrete grammar is at first, to obtain wavelet transformation curve maximal value max and minimum value min; Then, utilize formula $s^{\&prime;}\left(x\right)=\frac{(s\left(x\right)-\min )}{(\max -\min )}+1$ Carry out normalization, wherein s (x) is the wavelet transformation curve values before the normalization, and x is the coordinate figure of curve, and s ' is the curve values of the wavelet transformation after the normalized (x);

Resulting wavelet transformation curve carries out gradient horizontal projection curved scanning in the step 8. pair step 7, obtains minimum trough value and time position coordinates of small echo valley in the wavelet transformation curve; Concrete grammar is: seek the trough that satisfies certain condition from the starting point of curve, by relatively obtaining the position coordinates of minimum trough value and time small echo valley, satisfy certain condition and be meant that the trough value satisfies certain span less than certain value and trough width;

Step 9. utilizes that resulting wave trough position coordinate carries out the computing of car plate coarse positioning in the step 8, obtains the position coordinates of one or two license plate candidate area in including the vehicle image of car plate; Concrete grammar is: the minimum trough value of the curve that provides according to step 8 and time small echo valley position coordinates search for this trough of stating next-door neighbour about the position coordinates of two crests, the position coordinates correspondence of left side crest be the horizontal ordinate of the coboundary of car plate in including the vehicle image matrix of car plate, the horizontal ordinate of representing the position, coboundary of car plate herein with top_boundary, the right crest the position coordinates correspondence be the horizontal ordinate of the lower boundary of car plate in including the vehicle image of car plate, represent the horizontal ordinate of the lower boundary position of car plate herein with bot_boundary;

Step 10 is utilized in the step 9 resulting positional information to carry out the car plate rough lumber and is cut processing, obtains one or two license plate candidate area; Concrete grammar is, at first, the definition element is zero matrix mask, and the line number of mask is consistent with the line number and the columns of the vehicle gray level image matrix that includes car plate respectively with columns; Secondly, all be changed to 1 being positioned at top_boundary elements capable and bot_boundary all row in the ranks among the matrix mask, wherein top_boundary represents the abscissa value of the coboundary of car plate in including the vehicle gray level image of car plate, and bot_boundary represents the abscissa value of the lower boundary of car plate in vehicle image; Then, the element with same coordinate value in matrix mask and the vehicle gray level image matrix is multiplied each other, after multiplying each other, the element in the vehicle gray level image matrix in the non-license plate candidate area is zero, and the element value in the license plate candidate area remains unchanged; At last, the definition element is zero matrix I_cut, the line number of matrix I_cut is bot_boundary-top_boundary, columns and vehicle gray level image matrix column number equate, nonzero element in the matrix of consequence of matrix mask and vehicle gray level image matrix multiple is composed to I_cut, and matrix I_cut just represents license plate candidate area;

Resulting license plate candidate area carries out horizontal thunder and steps on conversion in the step 11. pair step 10, obtains 11 thunders and steps on transformation curve; Specifically be treated to: the definition horizontal direction is 0 degree, spends the car plate that every 1 degree rough lumber is cut out in the scopes of 5 degree-5 and carries out thunder and step on conversion, will obtain 11 thunders after the conversion and step on transformation curve;

The width value that step 12 utilizes 11 thunders in the step 11 to step on transformation curve is determined the coordinate figure of upper and lower side frame in license plate image of car plate; Concrete grammar is: resultant 11 thunders are stepped on the width of transformation curve in the comparison step 12, it is exactly that the angle of inclination of car plate is designated as R_hdegree that the thunder of width minimum is stepped on the pairing translation-angle of transformation curve, first crest value that this moment, thunder was stepped on the transformation curve is designated as R_top greater than the coordinate figure of the wire crest of certain value corresponding to the horizontal ordinate of car plate upper side frame in license plate image, thunder step on the transformation curve last crest value greater than the coordinate figure of the wire crest of certain value corresponding to the car plate lower frame in license plate image ordinate and be designated as R_bot;

Step 13 is utilized the license plate sloped angle that obtains in the step 12 and the coordinate information of upper and lower side frame, proofreaies and correct car plate and deletes the upper and lower side frame of car plate; Specifically be treated to: at first, proofread and correct car plate, if the angle of inclination R_hdegree of car plate is non-vanishing, thinks that then car plate tilts, thereby license plate image is rotated-R_hdegree proofreaies and correct car plate; Define an element then and be zero matrix R_hmask, total columns of this matrix and total line number equal total columns and the total line number of matrix I_cut respectively, the element of matrix R_hmask between R_top and R_bot is changed to one, and the element that has the same coordinate value among matrix R_hmask and the matrix I_cut is multiplied each other obtains a multiplied result matrix R_htbmask; At last, define an element and be zero matrix R_hcut, total line number of this matrix is R_bot-Rtop, total columns of total columns and matrix R_htbmask is identical, nonzero element among the matrix R_htbmask is composed to matrix R_hcut, and matrix R_hcut is exactly an image array of having deleted the license plate image correspondence of car plate upper and lower side frame;

The resulting image array of having deleted the car plate upper and lower side frame carries out vertical thunder and steps on conversion in the step 14. pair step 13, obtains 11 thunders and steps on transformation curve; Specifically be treated to: the definition vertical direction is 90 degree, spends in the scopes of 95 degree 85 and the car plate of deleting upper and lower side frame is carried out thunder steps on conversion every 1 degree, obtains 11 thunders after the conversion and steps on transformation curve;

Step 15 utilizes resultant 11 thunders in the step 14 to step on the crest value of transformation curve, determines the left and right side frame of car plate, and concrete grammar is: at first, find out a thunder of width minimum and step on transformation curve; On this curve, search for the wire crest of a crest value then from left to right greater than certain value, the coordinate figure of this crest is exactly that the ordinate of left frame in this license plate image matrix of having deleted the car plate of car plate upper and lower side frame is designated as R_left, turn left from the right side and search for the wire crest of a crest value greater than certain value, the coordinate figure of this crest is exactly that the ordinate of left frame in this license plate image matrix of having deleted the car plate upper and lower side frame is designated as R_right;

Step 16 is utilized the coordinate figure of the left and right side frame of the resulting car plate of having deleted upper and lower side frame in the step 15, the left and right side frame of deletion car plate; Concrete grammar is, at first, defines an element and is zero matrix R_vmask, and its total line number and total columns equate with total line number and the total columns of matrix R_hcut respectively, matrix R_vmask is positioned at the element that R_left row and R_right be listed as is changed to 1; Then the element that has the same coordinate value among matrix R_vmask and the matrix R_hcut is multiplied each other and obtain a matrix of consequence R_vlrmask; Define an element at last and be zero matrix R_vcut, total columns of this matrix is R_right-R_left, total line number of total line number and matrix R_vlrmask equates, nonzero element among the matrix R_vlrmask is composed to R_vcut, and matrix R_vcut has deleted the license plate image matrix of four frames up and down;

Resulting license plate image accurately carries out car plate color normalized in the step 17. pair step 16, but obtains a license plate image with black background and white number that includes identification number; Concrete grammar is at first, to add up white pixel number and black picture element number in the license plate area; Secondly compare black picture element number and white pixel number,, then carry out the gray scale upset if the white pixel number is most.

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