CN102201118A - Method for positioning boundaries of belt layers at tyre crown part of X-ray image of tyre - Google Patents

Method for positioning boundaries of belt layers at tyre crown part of X-ray image of tyre Download PDF

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Publication number
CN102201118A
CN102201118A CN2010106160468A CN201010616046A CN102201118A CN 102201118 A CN102201118 A CN 102201118A CN 2010106160468 A CN2010106160468 A CN 2010106160468A CN 201010616046 A CN201010616046 A CN 201010616046A CN 102201118 A CN102201118 A CN 102201118A
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belt
matrix
image
information
gray
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CN102201118B (en
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黄战华
刘正
都强
蔡怀宇
杭柏林
王孔茂
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Tianjin University
Mesnac Co Ltd
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Tianjin University
Mesnac Co Ltd
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Abstract

The invention relates to a method for positioning the boundaries of belt layers at a tyre crown part of an X-ray image of a tyre. The method comprises the steps of: positioning belt layers of all the belt layers at the tyre crown part of the X-ray image of a tyre, carrying out frequency-spectrum analysis on the tyre crown part of the X-ray image of the tyre with overlapped multiple belt layer textures, respectively processing frequency-spectrum information by using a Gaussian wedge-shaped filter with the angle between 0 degree and 179 degrees, restoring the extracted frequency-spectrum information into images, carrying out grey statistics to obtain gray statistical information vectors, combining all the gray statistical information vectors and converting the combined gray statistical information vectors into a gray information image, observing and selecting the gray information vectors which can be used for measuring and calculating the boundary information of all the belt layers, and calculating the boundary position coordinates of all the belt layers according to the selected gray information vectors.

Description

Be applied to tire x-ray image crown part position belt boundary alignment method
[technical field]
The invention belongs to the digital image processing techniques field, be specifically related to be applied to many texture images that crown position in the tire x-ray image forms because of many belts texture mutual superposition are carried out each belt boundary alignment.
[background technology]
Tire is that member is carried out in the main action of motor vehicles, and stablizing and whether meeting the Safety Design standard of tyre performance will directly determine to use the personal security of motor vehicles.Tire is the rotatable body of a kind of circular ring type of tubular section, and the rubber preformed material, the compounded rubber preformed material that are had all-steel cord by multilayer process through applying, moulding, sulfuration typing.The tire of described structure, the constituent material at its crown position inevitably exist problems such as all-steel cord density unevenness, wire of steel wire tire cord fracture, all-steel cord disappearance.Perspective imaging principle according to X-ray can obtain inside tires all-steel cord state and distribution situation, by to the state of all-steel cord and the analysis of distribution situation, obtains the information of defective, thereby realizes tire is declared level.
Existing tire X-ray machine test unit, in process of the test, tire is mounted, is clamped on four rotatable reference columns.In the time of the reference column rotation, drive the tire rotation, at this moment, the X-ray generator tube emission X-ray line by being positioned at the tire inboard penetrates tire, is received by the U type receiver that is positioned at the tire outside.
Existing tire X-ray machine test unit after obtaining the x-ray image of tire, is a foundation with the x-ray image of tire, declares level.Declare the observation of the main people of dependence of level at present, the tire x-ray image defective that is presented on the computer screen is carried out artificial cognition,, confirm the grade of tire by artificial intuitive judgment by naked eyes.At the crown position, because a plurality of belt mutual superposition are arranged, belt offset or scarce layer, the defective that can have a strong impact on tire x-ray image crown position is declared level.The belt that overlaps can be covered the distribution situation of each belt each other mutually, therefore at the crown position of tire x-ray image, with the naked eye observe the boundary position that is difficult to tell belt intuitively, the unascertainable words of the boundary position of belt, automatically declaring level at the defective of belt just is difficult to carry out, therefore need each belt be positioned before the computing machine automatic defect declares level carrying out, follow-up like this declare level work and could shoot the arrow at the target.
[summary of the invention]
The present invention seeks to overcome the prior art above shortcomings, a kind of tire x-ray image crown part position belt boundary alignment method that is applied to is provided.
The method that the present invention adopts Digital Image Processing is carried out boundary alignment to the belt at crown position, mark off the effective coverage of each belt, at follow-up declaring in the level work, help in concrete zone, to declare targetedly level operation and defect recognition, defective is divided by diverse location, different ownership, simultaneously help also to judge whether belt is offset, whether have the layer of lacking phenomenon to take place.
Spectrum analysis is carried out at the tire x-ray image crown position that the present invention is directed to many belts texture mutual superposition, Gauss's wedge shape wave filter is accepted or rejected spectrum information when adopting angle to be 0 ° to 179 ° respectively, the spectrum information that extracts is reduced to image and carries out gray-scale statistical, obtain the gray-scale statistical information vector, all gray-scale statistical information vectors are made up and are converted into the half-tone information image, by observing the half-tone information vector of selecting to can be used for calculating each belt boundary information, according to the half-tone information vector that is selected, calculate the boundary position coordinate of each belt, thereby each belt Boundary Recognition of mutual superposition is come out, realize the boundary alignment of each belt, implementation step is:
The first step is obtained a complete tire x-ray image I X, at this x-ray image I XInside also is simultaneously that its crown position intercepting size is 2 N* 2 NSquare-shaped image I OThe value of N requires: N is the integer greater than 1, has 2 simultaneously NLess than tire x-ray image I XWidth and 2 NLess than x-ray image I XHeight, and make I OWidth can cover whole crown position just, image I simultaneously OFour limits should be parallel to tire x-ray image I respectively XFour limits;
Second step is to first step truncated picture I OCarry out 2 dimension Fast Fourier Transform (FFT)s, obtain spectrum information matrix I F, with spectrum information matrix I FCarry out shifting function, and then to obtain with frequency (0,0) be 2 dimension spectrum information matrix I of central point FS, I F, I FSAll and image I OWith size, I F, I FSWith image I OMeet following relation:
I F=fft2(I O)
I FS=fftshift(I F)
Wherein fft2 represents to carry out quick 2 dimension Fourier transforms, and in the result images after the fft2 conversion, the pairing value of information of frequency (0,0) is corresponding to spectrum information matrix I FHeader element position, the upper left corner;
Fftshift represents spectrum information matrix I FCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, with the whole matrix that obtains that exchanges of 2,4 quadrants; After the exchange, the pairing value of information of frequency (0,0) is corresponding to the center of matrix;
In the 3rd step, utilize following Gauss's wedge shape wave filter formula, with spectrum information matrix I FSCarry out the filtering of Gauss's wedge shape,
G 1 ( x , y ) = e - ( θ - θ c ) 2 2 σ 2
G 2(x,y)=G 1(-x,-y)
G(x,y)=G 1(x,y)+G 2(x,y)
Wherein, θ CBe the angle center, its span is 0 °≤θ c<180 °
σ is a standard deviation, has determined the width of Gaussian curve on the circumferential direction of certain radius
θ is spectrum information matrix I FSIn certain first vegetarian refreshments be the summit with its frequency central point (0,0), be 0 ° angle value with horizontal dextrad direction, as 0 °≤θ cIn the time of<180 °, the θ span is-90 °≤θ≤270 °, and the computing method of θ are:
Work as I FSIn the coordinate of certain element (x when y) being positioned at 1,4 quadrants, has
θ = tan - 1 ( y x )
Work as I FSIn the coordinate of certain pixel (x when y) being positioned at 2,3 quadrants, has
θ = tan - 1 ( y x ) + 180
G is 2 dimension Gauss wedge shape wave filters, and this is one is the filtering template of former point symmetry, i.e. G about frequency central point (0,0) 1And G 2Be about frequency central point (0,0) symmetry;
At first make θ C=0 °, and according to the design parameter of each belt, the σ parameter of given Gauss's wedge shape wave filter is utilized Gauss's wedge shape filtering formula, generates the Gauss's wedge shape wave filter G about (0,0) former point symmetry;
The 4th step is with spectrum information matrix I FSG carries out dot product with Gauss's wedge shape wave filter, obtains spectrum information matrix I FL, this matrix has at utmost kept direction, the spectrum information of expectation texture, has abandoned direction, the spectrum information of other textures, and the dot product formula is as follows;
I FL=I FS·G
The 5th step is with spectrum information matrix I FLCarry out shifting function, and carry out inverse fast Fourier transform, thereby with I FLBe reduced to image I R, I FLWith I RSatisfy following relation:
I R=ifft2(fftshift(I FL))
Wherein, fftshift represents spectrum information matrix I FLCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, the whole exchange of 2,4 quadrants is obtained new matrix; Before the exchange, the pairing value of information of frequency (0,0) is corresponding to spectrum information matrix I FLThe center; After the exchange, the pairing value of information of frequency (0,0) is corresponding to the header element position, the upper left corner of matrix of consequence,
Ifft2 represents to carry out quick 2 dimension inverse Fourier transforms,
Through the image I after the filtering reduction R, farthest kept the texture of certain direction, and the texture information of other directions is weakened;
In the 6th step, calculate I RPixel grey scale average on the vertical direction of each horizontal ordinate position obtains one 1 vector of tieing up Computing formula is as follows:
V θ C ( i ) = 1 2 N Σ m = 1 2 N I R ( i , m ) , i = 1,2 , . . . , 2 N
Wherein, I R(i, m) presentation video I RIn certain pixel (i represents abscissa value for i, grey scale pixel value m), and m represents ordinate value;
In the 7th step, make θ again CBe respectively 1 °, 2 °, 3 ° ... 179 °, repeat the 3rd and went on foot for the 6th step, obtain
V θ(i) θ=0,1,…,179;i=1,2,…2 N
And with following formula vector set V θ(i) θ=0,1 ..., 179; I=1,2 ... 2 NBe rewritten as 2 dimension matrixes
V(θ,i) θ=0,1,…,179;i=1,2,…2 N
Being that matrix V is one 2 dimension matrix, highly is 180, and width is 2 N, this 2 dimension matrix is shown with image mode, can observe each belt boundary position distribution situation;
In the 8th step, the observation by to 2 dimension image V can pick out and the horizontal strip region of the some one to one high gray-scale values of each belt, and hypothesis has M belt here, and the horizontal strip region of then high gray-scale value is made as Zone iI=1,2, M, the number of the horizontal strip region of high gray-scale value is consistent with the belt number, simultaneously the horizontal strip region of each high gray-scale value is corresponding with some belts, and the width of the horizontal strip region of high gray-scale value equals the width of the belt corresponding with it, and the left and right sides boundary coordinate of the horizontal strip region of high gray-scale value equals the belt left and right sides border corresponding with it at I OIn coordinate, will with the horizontal strip region Zone of high gray-scale value iI=1,2 ..., the abscissa value β of M iI=1,2 ..., M extracts, can extract with each belt one to one I=1,2 ..., the M vector is at M
I=1,2 ..., the M vector can calculate M belt boundary information;
In the 9th step, calculate I=1,2 ..., the average of M I=1,2 ..., M is with average I=1,2 ..., M multiplication by constants coefficient lambda iI=1,2 ..., M obtains threshold value T iI=1,2 ..., M, promptly satisfy following relational expression:
T i = λ i V ‾ β i , i = 1,2 , . . . , M
At each vector I=1,2 ..., M, (being x=0) begins search from the left side, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x + 1 ) , x = 1,2 , . . . , 2 N - 1 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the left margin F on the border of asking LiI=1,2 ..., M;
In like manner, from vector I=1,2 ..., the right side of M (is x=2 N) beginning search left, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x - 1 ) , x = 2 N , 2 N - 1 , . . . , 2 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the right margin E on the border of asking RiI=1,2 ..., M;
At gray level image I OIn, respectively with I O(E Li, y), I O(E Ri, y) i=1,2 ..., M; Y=1,2 ..., 2 NThe pixel value gray-scale value is set to (255,255,255), i.e. boundary line of drawing each belt with white lines;
So far, finish the belt boundary alignment.
Advantage of the present invention and good effect
The present invention can pick out the border of each belt accurately, mark off the effective coverage of each belt, at follow-up declaring in the level work, help in concrete zone, to declare targetedly level operation and defect recognition, defective is divided by diverse location, different ownership, simultaneously help also to judge whether belt is offset, whether have the layer of lacking phenomenon to take place; What the method helped the tire x-ray image accurately declares level, improves accuracy rate and efficient that tire is declared level.
[description of drawings]
Fig. 1 is a tire x-ray image crown position intercepting synoptic diagram;
Fig. 2 is Gauss's wedge shape filtering template synoptic diagram;
Fig. 3 is the process flow diagram of described belt texture boundary alignment method;
Fig. 4 is the average statistical graph that obtains after filtering under certain angle direction;
Fig. 5 is after adopting 0 °~179 ° Gauss's wedge shape wave filters that the spectrum information of tire x-ray image crown part bit image is carried out filtering respectively, the gray-scale statistical information matrix that shows with image format that obtains;
Fig. 6 is boundary alignment final effect figure.
[embodiment]
As shown in Figure 1, tyre crown position many belts of the x-ray image texture separation method that is applied to of the present invention, its effective object is the subimage of the suitable size in tire x-ray image crown position.The size of subimage is 2 9* 2 9, subimage has covered the left hand edge and the right hand edge at crown position; Four limits of subimage are parallel to four limits of tire x-ray image simultaneously;
As shown in Figure 2, this is to adopt Gauss's wedge shape filtering formula, Gauss's wedge shape filtering template synoptic diagram of generation.This Gauss's wedge shape filtering template is about central point as can be known.
As shown in Figure 3, the concrete steps of tire x-ray image crown part position belt boundary alignment method are as follows:
The first step is obtained a complete tire x-ray image I X, at this x-ray image I XInside also is simultaneously that its crown position intercepting size is 2 N* 2 NSquare-shaped image I OThe value of N requires: N is the integer greater than 1, has 2 simultaneously NLess than tire x-ray image I XWidth and 2 NLess than x-ray image I XHeight, and make I OWidth can cover whole crown position just, image I simultaneously OFour limits should be parallel to tire x-ray image I respectively XFour limits;
Second step is to first step truncated picture I OCarry out 2 dimension Fast Fourier Transform (FFT)s, obtain spectrum information matrix I F, with spectrum information matrix I FCarry out shifting function, and then to obtain with frequency (0,0) be 2 dimension spectrum information matrix I of central point FS, I F, I FSAll and image I OWith size, I F, I FSWith image I OMeet following relation:
I F=fft2(I O)
I FS=fftshift(I F)
Wherein fft2 represents to carry out quick 2 dimension Fourier transforms, and in the result images after the fft2 conversion, the pairing value of information of frequency (0,0) is corresponding to spectrum information matrix I FHeader element position, the upper left corner.
Fftshift represents I FCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, with the whole matrix that obtains that exchanges of 2,4 quadrants.After the exchange, the pairing value of information of frequency (0,0) is corresponding to the center of matrix.
In the 3rd step, utilize following Gauss's wedge shape wave filter formula, with spectrum information matrix I FSCarry out the filtering of Gauss's wedge shape,
G 1 ( x , y ) = e - ( θ - θ c ) 2 2 σ 2
G 2(x,y)=G 1(-x,-y)
G(x,y)=G 1(x,y)+G 2(x,y)
Wherein, θ CBe the angle center, its span is 0 °≤θ c<180 °
σ is a standard deviation, has determined the width of Gaussian curve on the circumferential direction of certain radius
θ is I FSIn certain first vegetarian refreshments be the summit with its frequency central point (0,0), be 0 ° angle value with horizontal dextrad direction, as 0 °≤θ cIn the time of<180 °, the θ span is-90 °≤θ≤270 °, and the computing method of θ are:
Work as I FSIn the coordinate of certain element (x when y) being positioned at 1,4 quadrants, has
θ = tan - 1 ( y x )
Work as I FSIn the coordinate of certain pixel (x when y) being positioned at 2,3 quadrants, has
θ = tan - 1 ( y x ) + 180
G is 2 dimension Gauss wedge shape wave filters, and this is one is the filtering template of former point symmetry, i.e. G about frequency central point (0,0) 1And G 2Be about frequency central point (0,0) symmetry;
At first make θ C=0 °, and according to the design parameter of each belt, the σ parameter of given Gauss's wedge shape wave filter is utilized Gauss's wedge shape filtering formula, generates the Gauss's wedge shape wave filter G about (0,0) former point symmetry;
The 4th step is with spectral matrix I FSG carries out dot product with Gauss's wedge shape wave filter, obtains spectrum information matrix I after the filtering FL, this matrix has at utmost kept direction, the spectrum information of expectation texture, has abandoned direction, the spectrum information of other textures, and the dot product formula is as follows;
I FL=I FS·G
The 5th step is with filtered spectrum information matrix I FLCarry out shifting function, and carry out inverse fast Fourier transform, thereby with I FLBe reduced to image I R, I FLWith I RSatisfy following relation:
I R=ifft2(fftshift(I FL))
Wherein, fftshift represents I FLCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, the whole exchange of 2,4 quadrants is obtained new matrix.Before the exchange, the pairing value of information of frequency (0,0) is corresponding to I FLThe center; After the exchange, the pairing value of information of frequency (0,0) is corresponding to the header element position, the upper left corner of matrix of consequence,
Ifft2 represents to carry out quick 2 dimension inverse Fourier transforms,
Through the image I after the filtering reduction R, farthest kept the texture of certain direction, and the texture information of other directions is weakened;
In the 6th step, calculate I RPixel grey scale average on the vertical direction of each horizontal ordinate position obtains one 1 vector of tieing up Computing formula is as follows:
V θ C ( i ) = 1 2 N Σ m = 1 2 N I R ( i , m ) , i = 1,2 , . . . , 2 N
Wherein, I R(i, m) presentation video I RIn certain pixel (i represents abscissa value for i, grey scale pixel value m), and m represents ordinate value;
In the 7th step, make θ again CBe respectively 1 °, 2 °, 3 ° ... 179 °, repeat the 3rd and went on foot for the 6th step, obtain
V θ(i) θ=0,1,…,179;i=1,2,…2 N
And with following formula vector set V θ(i) θ=0,1 ..., 179; I=1,2 ... 2 NBe rewritten as 2 dimension matrixes
V(θ,i) θ=0,1,…,179;i=1,2,…2 N
Being that matrix V is one 2 dimension matrix, highly is 180, and width is 2 N, this 2 dimension matrix is shown with image mode, can observe each belt boundary position distribution situation;
In the 8th step, the observation by to 2 dimension image V can pick out and the horizontal strip region of the some one to one high gray-scale values of each belt, and hypothesis has M belt here, and the horizontal strip region of then high gray-scale value is made as Zone i
I=1,2, M, the number of the horizontal strip region of high gray-scale value is consistent with the belt number, simultaneously the horizontal strip region of each high gray-scale value is corresponding with some belts, and the width of the horizontal strip region of high gray-scale value equals the width of the belt corresponding with it, and the left and right sides boundary coordinate of the horizontal strip region of high gray-scale value equals the belt left and right sides border corresponding with it at I OIn coordinate, will with the horizontal strip region Zone of high gray-scale value iI=1,2 ..., the abscissa value β of M i
I=1,2 ..., M extracts, can extract with each belt one to one I=1,2 ..., the M vector is at M I=1,2 ..., the M vector can calculate M belt boundary information;
In the 9th step, calculate I=1,2 ..., the average of M I=1,2 ..., M is with average I=1,2 ..., M multiplication by constants coefficient lambda iI=1,2 ..., M obtains threshold value T iI=1,2 ..., M, promptly satisfy following relational expression:
T i = λ i V ‾ β i , i = 1,2 , . . . , M
At each vector I=1,2 ..., M, (being x=0) begins search from the left side, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x + 1 ) , x = 1,2 , . . . , 2 N - 1 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the left margin E on the border of asking LiI=1,2 ..., M;
In like manner, from vector I=1,2 ..., the right side of M (is x=2 N) beginning search left, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x - 1 ) , x = 2 N , 2 N - 1 , . . . , 2 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the right margin E on the border of asking RiI=1,2 ..., M;
At gray level image I OIn, respectively with I O(E Li, y), I O(E Ri, y) i=1,2 ..., M; Y=1,2 ..., 2 NThe pixel value gray-scale value is set to (255,255,255), i.e. boundary line of drawing each belt with white lines;
So far, finish the belt boundary alignment.
As shown in Figure 4, this is after adopting certain Gauss's wedge shape wave filter that filtering is carried out at tire x-ray image crown position, the gray-scale statistical curve of the filtering result images that obtains; Horizontal ordinate has been represented the width of image, and ordinate is represented the pixel grey scale average at certain horizontal ordinate place of filtering result images.
As shown in Figure 5, this is the gray-scale statistical matrix of being made up of 180 gray-scale statisticals vector that shows with image format, 1,2, No. 3 high gray-scale value zone correspondence wherein 3 different belts.As can be seen, the boundary position in 3 high grey value profile zones is that significantly the border, the left and right sides of extracting these 3 high gray-scale value zones can obtain the border, the left and right sides of 3 belts.
As shown in Figure 6, the 3 pairs of vertical straight lines of white that on the crown part bit image of tire x-ray image, draw, the corresponding border, the left and right sides of 3 belts.

Claims (2)

1. one kind is applied to tire x-ray image crown part position belt boundary alignment method, it is characterized in that: spectrum analysis is carried out at the tire x-ray image crown position at many belts texture mutual superposition, Gauss's wedge shape wave filter is accepted or rejected spectrum information when adopting angle to be 0 ° to 179 ° respectively, the spectrum information that extracts is reduced to image and carries out gray-scale statistical, obtain the gray-scale statistical information vector, all gray-scale statistical information vectors are made up and are converted into the half-tone information image, by observing the half-tone information vector of selecting to can be used for calculating each belt boundary information, according to the half-tone information vector that is selected, calculate the boundary position coordinate of each belt, thereby each belt Boundary Recognition of mutual superposition is come out, realize the boundary alignment of each belt, implementation step is:
The first step is obtained a complete tire x-ray image I X, at this x-ray image I XInside also is simultaneously that its crown position intercepting size is 2 N* 2 NSquare-shaped image I OThe value of N requires: N is the integer greater than 1, has 2 simultaneously NLess than tire x-ray image I XWidth and 2 NLess than x-ray image I XHeight, and make I OWidth can cover whole crown position just, image I simultaneously OFour limits should be parallel to tire x-ray image I respectively XFour limits;
Second step is to first step truncated picture I OCarry out 2 dimension Fast Fourier Transform (FFT)s, obtain spectrum information matrix I F, with spectrum information matrix I FCarry out shifting function, and then to obtain with frequency (0,0) be 2 dimension spectrum information matrix I of central point FS, I F, I FSAll and image I OWith size, I F, I FSWith image I OMeet following relation:
I F=fft2(I O)
I FS=fftshift(I F)
Wherein fft2 represents to carry out quick 2 dimension Fourier transforms, and in the result images after the fft2 conversion, the pairing value of information of frequency (0,0) is corresponding to spectrum information matrix I FHeader element position, the upper left corner;
Fftshift represents spectrum information matrix I FCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, with the whole matrix that obtains that exchanges of 2,4 quadrants; After the exchange, the pairing value of information of frequency (0,0) is corresponding to the center of matrix;
In the 3rd step, utilize following Gauss's wedge shape wave filter formula, with spectrum information matrix I FSCarry out the filtering of Gauss's wedge shape,
G 1 ( x , y ) = e - ( θ - θ c ) 2 2 σ 2
G 2(x,y)=G 1(-x,-y)
G(x,y)=G 1(x,y)+G 2(x,y)
Wherein, θ CBe the angle center, its span is 0 °≤θ c<180 °
σ is a standard deviation, has determined the width of Gaussian curve on the circumferential direction of certain radius
θ is spectrum information matrix I FSIn certain first vegetarian refreshments be the summit with its frequency central point (0,0), be 0 ° angle value with horizontal dextrad direction, as 0 °≤θ cIn the time of<180 °, the θ span is-90 °≤θ≤270 °, and the computing method of θ are:
Work as I FSIn the coordinate of certain element (x when y) being positioned at 1,4 quadrants, has
θ = tan - 1 ( y x )
Work as I FSIn the coordinate of certain pixel (x when y) being positioned at 2,3 quadrants, has
θ = tan - 1 ( y x ) + 180
G is 2 dimension Gauss wedge shape wave filters, and this is one is the filtering template of former point symmetry, i.e. G about frequency central point (0,0) 1And G 2Be about frequency central point (0,0) symmetry;
At first make θ C=0 °, and according to the design parameter of each belt, the σ parameter of given Gauss's wedge shape wave filter is utilized Gauss's wedge shape filtering formula, generates the Gauss's wedge shape wave filter G about (0,0) former point symmetry;
The 4th step is with spectrum information matrix I FSG carries out dot product with Gauss's wedge shape wave filter, obtains spectrum information matrix I FL, this matrix has at utmost kept direction, the spectrum information of expectation texture, has abandoned direction, the spectrum information of other textures, and the dot product formula is as follows;
I FL=I FS·G
The 5th step is with spectrum information matrix I FLCarry out shifting function, and carry out inverse fast Fourier transform, thereby with I FLBe reduced to image I R, I FLWith I RSatisfy following relation:
I R=ifft2(fftshift(I FL))
Wherein, fftshift represents spectrum information matrix I FLCarrying out shifting function, is initial point with the matrix center promptly, does level and vertical coordinate axis, with the whole exchange of 1,3 quadrants, the whole exchange of 2,4 quadrants is obtained new matrix; Before the exchange, the pairing value of information of frequency (0,0) is corresponding to spectrum information matrix I FLThe center; After the exchange, the pairing value of information of frequency (0,0) is corresponding to the header element position, the upper left corner of matrix of consequence,
Ifft2 represents to carry out quick 2 dimension inverse Fourier transforms,
Through the image I after the filtering reduction R, farthest kept the texture of certain direction, and the texture information of other directions is weakened;
In the 6th step, calculate I RPixel grey scale average on the vertical direction of each horizontal ordinate position obtains one 1 vector of tieing up Computing formula is as follows:
V θ C ( i ) = 1 2 N Σ m = 1 2 N I R ( i , m ) , i = 1,2 , . . . , 2 N
Wherein, I R(i, m) presentation video I RIn certain pixel (i represents abscissa value for i, grey scale pixel value m), and m represents ordinate value;
In the 7th step, make θ again CBe respectively 1 °, 2 °, 3 ° ... 179 °, repeat the 3rd and went on foot for the 6th step, obtain
V θ(i) θ=0,1,…,179;i=1,2,…2 N
And with following formula vector set V θ(i) θ=0,1 ..., 179; I=1,2 ... 2 NBe rewritten as 2 dimension matrixes
V(θ,i) θ=0,1,…,179;i=1,2,…2 N
Being that matrix V is one 2 dimension matrix, highly is 180, and width is 2 N, this 2 dimension matrix is shown with image mode, can observe each belt boundary position distribution situation;
In the 8th step, the observation by to 2 dimension image V can pick out and the horizontal strip region of the some one to one high gray-scale values of each belt, and hypothesis has M belt here, and the horizontal strip region of then high gray-scale value is made as Zone iI=1,2, M, the number of the horizontal strip region of high gray-scale value is consistent with the belt number, simultaneously the horizontal strip region of each high gray-scale value is corresponding with some belts, and the width of the horizontal strip region of high gray-scale value equals the width of the belt corresponding with it, and the left and right sides boundary coordinate of the horizontal strip region of high gray-scale value equals the belt left and right sides border corresponding with it at I OIn coordinate, will with the horizontal strip region Zone of high gray-scale value iI=1,2 ..., the abscissa value β of M iI=1,2 ..., M extracts, can extract with each belt one to one I=1,2 ..., the M vector is at M
I=1,2 ..., the M vector can calculate M belt boundary information;
In the 9th step, calculate I=1,2 ..., the average of M I=1,2 ..., M is with average I=1,2 ..., M multiplication by constants coefficient lambda iI=1,2 ..., M obtains threshold value T iI=1,2 ..., M, promptly satisfy following relational expression:
T i = λ i V ‾ β i , i = 1,2 , . . . , M
At each vector I=1,2 ..., M, (being x=0) begins search from the left side, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x + 1 ) , x = 1,2 , . . . , 2 N - 1 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the left margin E on the border of asking LiI=1,2 ..., M;
In like manner, from vector I=1,2 ..., the right side of M (is x=2 N) beginning search left, when the first fit condition
V β i ( x ) ≤ T i ≤ V β i ( x - 1 ) , x = 2 N , 2 N - 1 , . . . , 2 ; i = 1,2 , . . . , M
The time, then Ci Shi x is designated as the right margin E on the border of asking RiI=1,2 ..., M;
At gray level image I OIn, respectively with I O(E Li, y), I O(E Ri, y) i=1,2 ..., M; Y=1,2 ..., 2 NThe pixel value gray-scale value is set to (255,255,255), i.e. boundary line of drawing each belt with white lines;
So far, finish the belt boundary alignment.
2. method according to claim 1 is characterized in that: be applied to separability that tire x-ray image crown part position belt boundary alignment method utilized spectrum information to realize the belt boundary alignment, separability is meant:
Crown position at the tire x-ray image, a plurality of belt texture mutual superposition, what therefore the crown position of the x-ray image of tire demonstrated is the texture of mutual each the belt all-steel cord that overlaps, the border of each belt is covered by each belt texture of mutual superposition, tire x-ray image crown part bit image I OIn each belt include different texture informations, contain frequency spectrum, direction etc., to tire x-ray image crown part bit image I OAfter carrying out Fast Fourier Transform (FFT) and displacement, the spectrum information matrix I that obtains FSIn, the texture information correspondence that different belt comprised spectrum information matrix I FSIn different local locations, the spectrum information that is in different local locations is separable, extracts spectrum information matrix I by Gauss's wedge shape wave filter FSIn the information of certain regional area, just can obtain the texture information of expectation.
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