CN106097430B - A kind of laser stripe center line extraction method of more gaussian signal fittings - Google Patents
A kind of laser stripe center line extraction method of more gaussian signal fittings Download PDFInfo
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Abstract
The present invention is to provide a kind of laser stripe center line extraction methods of more gaussian signal fittings.The gaussian signal that the method for the present invention has different expectations and method by using one group, got up with different positions and weighed combination, the distribution of fitting laser line image one-line pixel value to the greatest extent, when finding best fitting combination, the optimal estimation to laser lines center can be made the location of when being combined according to each gaussian signal.The method of the present invention can be solved effectively in the case where the pixel Distribution value of laser line image cannot be satisfied symmetry and homogeneity, accurately estimate the center of laser lines, improve the precision of 3D scan rebuildings.
Description
Technical field
The processing method for the inexpensive 3D scanning systems based on laser lines that the present invention relates to a kind of, and in particular to one
The laser lines center extraction method that kind is fitted based on gaussian signal.
Background technology
Three-dimension object recognition technology is in the numerous areas such as robot workpiece grabbing, self-navigation, automatic detection, medical analysis
It has a wide range of applications.And 3D scanning systems, as most important data acquisition means, acquisition precision will directly affect identification
Success or not.
Currently based on the contactless 3D scanning systems precision height of structure light, have a wide range of application, but its expensive cost limit
The universal of it has been made, therefore has had more wide market and research empty with the inexpensive measuring system of laser in place structure light
Between.
In a 3D scanning system based on laser, since there are camera acquisition precision, laser stripes to have centainly
Width and testee surface irregularity lead to reflective scattering, how accurately to extract laser stripe center will be straight
Connect the acquisition precision of decision systems.
Currently used lines center extraction algorithm has：Extremum method, geometrical center method, image thinning method, is based on threshold method
Least square method, Hessian matrix methods of Gaussian Profile etc..
Invention content
The purpose of the present invention is to provide a kind of center line that can accurately extract laser stripe image, make to be based on laser
3D scanning systems have higher acquisition precision more gaussian signals be fitted laser stripe center line extraction method.
The object of the present invention is achieved like this：
Step 1：The image I for including laser lines is obtained by image capture device, the resolution ratio of image I is Ymax×
Xmax；
Step 2：If N (the N ∈ [ of image I;1,Xmax]) row pixel value constitute sequence be initial data MN(j),(j
∈[1,Ymax]), input signal m is obtained by a cube interpolation algorithmN(t), wherein t=nTs, TsFor interpolation interval, N ∈ [1,
Xmax],j∈[1, Ymax]；
Step 3：For the data after the laser lines pixel value normalization of the Nth row of image I, one range of setting exists
This pixel is labeled as 1, is less than the threshold value by the threshold value between 0.2 to 0.5 when pixel Value Data is more than the threshold value
When, then it is labeled as 0；All mean values for marking the row serial number belonging to the pixel for being are calculated, using this mean value as initial center point
u0, while the difference for the pixel point range serial number maxima and minima for being labeled as 1 is calculated, using this difference as initial width of fringe
w0；
Step 4：Build the one group of M substrate gaussian signal set G for fitting with different expectations and variance, collection
The length of all substrate gaussian signals in conjunction and input signal mN(t) identical, and set zi=2 σi 2, σiIt is high for i-stage substrate
The standard deviation of this signal, and by ziIt is named as the dilatability of i-stage substrate gaussian signal, enables and it is expected ui=u0+si, and by ui
It is named as the symmetrical centre of i-stage substrate gaussian signal, wherein u0For initial center point, siFor offset；
The initial center point u that will be obtained in step 30Symmetrical centre u as first order substrate gaussian signal0, i.e. u1=
u0, initial width of fringe w0Dilatability for determining first order substrate gaussian signalWherein q is constant,
Thus the one group of M substrate gaussian signal set G with different dilatabilities are obtained.
In above formula, gzi,ui(x) it is i-stage substrate gaussian signal, and its dilatability is zi, symmetrical centre ui。
Step 5：By calculating maximum cross-correlation coefficient, the similitude of each substrate gaussian signal and input signal is acquired：
Wherein, rzi(τi) be input signal and i-stage substrate gaussian signal maximum cross-correlation value, τiIt is mutual for two signals
When pass value is maximized, the relative position of two signals is poor；
Step 6：Define artificial signal gf(x) it is the m substrate Gaussian functions with different dilatabilities and symmetrical centre,
According to the signal after one group of fixed fitting weights superposition, and initial m=2 being set, fitting weights are one group of fixed constant,
Its selection should follow dilatability ziIt is bigger, it is fitted this bigger principle of weights；
Step 7：According to the following formula, calculating is formed by stacking by substrate gaussian signal by one group of fixed fitting weights artificial
Signal gf(t),
Wherein φi(i∈[1,M]) it is fitting weights, it is one group of fixed constant,
Change the inclined of afterbody i.e. i-th (i=m) the grade substrate gaussian signal for forming this artificial signal in a certain range
Shifting amount si, artificial signal is recalculated, is further continued for calculating artificial signal and the cross correlation measure r of input signalf(τm)：
rf(τm)=max (cov<mN(t),gf(t)>)=max (∑ mN(τ)gf(t+τ))
Finding makes cross correlation measure rf(τm) it is maximum when offset si, it is s to add this offset at this timeiSubstrate Gauss letter
Number when, artificial signal is the most similar to input signal, τmFor cross correlation measure maximum when artificial signal and position input signal it is poor；
Step 8：Make m=m+1, repeat step 7, until m=M, that is, added afterbody substrate Gaussian function；
Step 9：When the symmetrical centre offset of each substrate gaussian signal is respectively s1,s2,s3,…smWhen, by these
The artificial signal that substrate gaussian signal is composed has highest similarity with input signal, makes rfThe τ that (τ) is maximized is
Integral position is poor, calculates the optimal estimation p to central point：
Wherein λi(i∈[1,M]) be i-stage substrate gaussian signal symmetrical centre Combining weights, which is
One group of fixed constant, its selection should follow dilatability ziIt is bigger, this bigger principle of Combining weights, uiFor each substrate Gauss
The symmetrical centre of signal, rziThe substrate gaussian signal of the variant dilatability obtained for step 5 and the maximum of input signal are mutually
Pass value, rziIt is higher, then illustrate to include more dilatabilities in input signal to be ziSubstrate gaussian signal ingredient, ηiTo correct
Proportionality constant, the significance level of the symmetrical centre of the Gaussian function for reacting different dilatabilities；
Step 10：Step 2 is repeated to step 9, until all X have been calculatedmaxCapable central point is obtained to laser lines
The optimal estimation of picture centre line.
The method of the present invention mainly uses approximating method of one group of gaussian signal to former laser line image cross section.Pass through
The use of the Gaussian function with different mean and variances is basis function, by certain rule combination superposition, comes farthest
It is fitted the distribution of collected laser stripe image cross section.When finding best fitting result, according to each gaussian signal
Symmetrical centre extrapolates the optimal estimation to laser stripe center by certain rule.
The advantages of the present invention over the prior art are that：Existing method largely assumes the cross section of laser line image
The distribution of pixel there are the properties such as symmetry and homogeneity, and during actual acquisition, due to incident angle, object being measured
Caused by surface irregularity the problems such as refraction scattering, image capture device noise, the pixel of the cross section of laser line image
Distribution is difficult to meet general symmetry and homogeneity.Whether the method for the present invention is full independent of the distribution of the pixel of cross section
The two characteristics of foot are fitted the pixel Distribution value of cross section by using multiple gaussian signals of different expectations and variance, look for
To when making cross correlation maximum, the relative position of each difference gaussian signals, so that it is determined that the center of laser lines.Greatly carry
The computational accuracy of high center line.
Description of the drawings
Fig. 1 is the method for the present invention flow chart.
Fig. 2 is the object being measured for test.
Fig. 3 is laser stripe image instance 1.
Fig. 4 is example 1 certain one-line pixel value distribution and to the input signal distribution map after its interpolation, is given simultaneously
The initial center point being calculated using threshold method.
Fig. 5 is when being fitted using two-stage gaussian signal to example 1 row pixel value, the offset of second level gaussian signal with
The relational graph of maximum cross-correlation coefficient.
Fig. 6 is the optimal result schematic diagram being fitted using two-stage gaussian signal to the row pixel value of example 1.
When Fig. 7 is to the row pixel value addition third level gaussian signal fitting of example 1, the offset of third level gaussian signal
With the relational graph of maximum cross-correlation coefficient.
Fig. 8 is the optimal result schematic diagram being fitted using three-level gaussian signal to the row pixel value of example 1.
Fig. 9 is the central line pick-up result figure to example 1.
Figure 10 is the difference of the center line result and ideal situation extracted using the method for the present invention.
Figure 11 is the central line pick-up result figure to example 1 using geometrical center method.
Figure 12 is the difference of the center line result and ideal situation extracted using geometrical center method.
Figure 13 is the vertical view for carrying out 3D reconstructions to testee using the method for the present invention extraction center line.
Figure 14 is the vertical view for carrying out 3D reconstructions to testee using geometrical center method extraction center line.
Specific implementation mode
The present invention is described in detail for citing below in conjunction with the accompanying drawings.
The method of the present invention has the requirement of higher-symmetry independent of laser line image pixel Distribution value, by using
Difference it is expected and multiple gaussian signals of variance are to be fitted the pixel Distribution value of laser lines uniline, and finding keeps cross correlation maximum
When, the relative position of each difference gaussian signal, so that it is determined that the center of the row laser lines.Laser line image institute has been calculated
There is capable laser rays sliver transvers section central point, you can acquire the center line of laser line image, detailed process includes the following steps：
(1) testee as shown in Figure 2 is measured using the 3D measuring systems based on laser, obtains laser line image sequence
In a certain image instance 1, as shown in Figure 3.
(2) it takes in example 1 that nth row of pixels value sequence is as initial data, for ease of calculation, place is first normalized
Reason, as shown in circular hollow point in Fig. 4.Since initial data is more sparse, cross correlation measure is calculated in step (7) in order to improve
Accuracy, to initial data carry out interpolation operation, obtain more intensive input signal, as shown in lines in Fig. 4 shown in.
(3) use threshold method that the initial data after normalization, threshold value of the range between 0.2 to 0.5 is set,
The pixel value that will be greater than the threshold value is labeled as 1, and the pixel value for being less than the threshold value is labeled as 0, calculates the pixel that pixel value is labeled as 1
Row coordinate mean value where point, i.e. initial center point u0, as shown in Fig. 4 stellate points and maximum column coordinate is sat with minimum row
Target difference, i.e. width w0。
(4) central point and width obtained according to step (3) determines the one group of total M substrate gaussian signals for fitting,
It is shown below.
uiFor the symmetrical centre of i-stage gaussian signal, siFor the opposite initial center point of symmetrical centre of i-stage gaussian signal
Offset.σiFor the standard deviation of i-stage gaussian signal, definitionFor the dilatability of i-stage gaussian signal, and first
The dilatability of grade gaussian signal determines that q is constant by the width in step (3).
(5) this group of substrate gaussian signal and the maximum cross-correlation coefficient of input signal are calculated：
(6) the m Gaussian functions with different dilatabilities and symmetrical centre are defined according to one group of fixed fitting weights
Signal after superposition is artificial signal gf(x), fitting weights are one group of fixed constant, it, which chooses, should follow dilatability ziIt is bigger,
It is fitted this bigger principle of weights.And initial m=2 is set, first order gaussian signal offset s1=0.
(7) according to the following formula, the artificial signal being formed by stacking by one group of fixed fitting weights by substrate gaussian signal is calculated
gf(t)。
Wherein φi(i∈[1,M]) it is fitting weights, it is one group of fixed constant.
Using 0.1 pixel wide as step-length in a certain range (being 8 pixel unit width in this example), composition is altered in steps
The offset s of the afterbody of this artificial signal i.e. i-th (i=m) grade gaussian signali, calculate after often changing an offset
The cross correlation measure of artificial signal and input signal：
rf(τm)=max (cov<mN(t),gf(t)>)=max (∑ mN(τ)gf(t+τ)) (4)
Finding makes cross correlation measure rf(τm) it is maximum when offset si, it is s to add this offset at this timeiGaussian signal when,
Artificial signal is the most similar to input signal, and τ at this timemFor cross correlation measure maximum when artificial signal and position input signal it is poor.
(8) make m=m+1, repeat step 7.Until m=M, that is, added afterbody substrate Gaussian function.
When carrying out center line computation to example 1, example 1 the is fitted using the different gaussian signal of three-level (i.e. M=3)
The pixel Distribution value of N rows when Fig. 5 is m=2, changes the offset s of second level gaussian signal symmetrical centre2Obtained artificial letter
Relationship number with the cross-correlation coefficient of input signal works as s in this example2When=- 1.8, artificial signal has higher with input signal
Similarity.Fig. 6 is the optimal fitting that is superimposed with second level gaussian signal using first order gaussian signal as a result, thick line is artificial
Signal, black thin are input signal to be fitted.When Fig. 7 is m=M=3, change the inclined of third level gaussian signal symmetrical centre
Shifting amount s3The relationship of the cross-correlation coefficient of obtained artificial signal and input signal works as s in this example3When=1.1, artificial signal with
Input signal has higher similarity.Fig. 8 is to make the optimal fitting of three-level gaussian signal superposition as a result, thick line is artificial letter
Number, black thin is input signal to be fitted.
From figure 8, it is seen that artificial signal has higher similitude with input signal.
(9) optimal estimation to central point is calculated：
Artificial signal is set to believe to each Gauss of input signal when the most similar to what step (8) obtained according to step (7)
Number symmetrical centre offset si, calculate the symmetrical centre u of each gaussian signali=u0+si.Artificial signal and input are calculated again
The maximum cross-correlation coefficient r of signalfDisplacement τ when (τ) is maximized finally is calculated according to formula (5) to the optimal of central point
Estimate p：
Wherein λiFor the weights of variant center of energy, which is one group of fixed constant, its selection should be abided by
Follow dilatability ziIt is bigger, this bigger principle of Combining weights, riIt is similar to input signal for the Gaussian function of variant dilatability
Degree, riIt is higher, then illustrate to include more dilatabilities in input signal to be ziGaussian signal ingredient, ηiIt is normal to correct ratio
Number, the significance level of the center of energy of the Gaussian function for reacting different dilatabilities.
To in the central point result such as Fig. 8 of 1 Nth row laser line image of example shown in black * shape points in this example.
(10) step (2) is repeated to step (9), and all X have been calculatedmaxCapable central point, you can obtain to laser stripe figure
As the calculating of the center line of example 1, the results are shown in Figure 9.The difference with ideal situation is given simultaneously, as shown in Figure 10.
In order to further compare the validity of the method for the present invention, we carry out example 1 using traditional geometrical center method
Center line computation.Laser lines center line result is as shown in figure 11.It is as shown in figure 12 with ideal situation difference.Finally, we are right
All laser line images carry out central line pick-up, and according to the parameter demarcated, three are carried out to testee as shown in Figure 2
Dimension is rebuild, and Figure 13 is the result that the method for the present invention calculates center line and reconstruction.Figure 14 is to calculate center line using geometrical center method
And rebuild as a result, in terms of reconstructed results, the reconstructed results details based on the method for the present invention is clear, performance is abundant, has higher
Reduction degree.
Claims (1)
1. a kind of laser stripe center line extraction method of more gaussian signal fittings, it is characterized in that：
Step 1：The image I for including laser lines is obtained by image capture device, the resolution ratio of image I is Ymax×Xmax；
Step 2：If N (the N ∈ [ of image I;1,Xmax]) row pixel value constitute sequence be initial data MN(j),(j∈[1,
Ymax]), input signal m is obtained by a cube interpolation algorithmN(t), wherein t=nTs, TsFor interpolation interval, N ∈ [1, Xmax],j∈
[1, Ymax]；
Step 3：For the data after the laser lines pixel value normalization of the Nth row of image I, one range of setting is arrived 0.2
This pixel is labeled as 1, when being less than the threshold value, then by the threshold value between 0.5 when pixel Value Data is more than the threshold value
Labeled as 0；All mean values for marking the row serial number belonging to the pixel for being are calculated, using this mean value as initial center point u0, simultaneously
The difference for calculating the pixel point range serial number maxima and minima for being labeled as 1, using this difference as initial width of fringe w0；
Step 4：The substrate gaussian signal set G for fitting of one group of M with different expectations and variance are built, in set
All substrate gaussian signals length and input signal mN(t) identical, and set zi=2 σi 2, σiBelieve for i-stage substrate Gauss
Number standard deviation, and by ziIt is named as the dilatability of i-stage substrate gaussian signal, enables and it is expected ui=u0+si, and by uiName
For the symmetrical centre of i-stage substrate gaussian signal, wherein u0For initial center point, siFor offset；
The initial center point u that will be obtained in step 30Symmetrical centre u as first order substrate gaussian signal0, i.e. u1=u0, just
Beginning width of fringe w0Dilatability for determining first order substrate gaussian signalWherein q is constant, thus
To the one group of M substrate gaussian signal set G with different dilatabilities,
In above formula, gzi,ui(x) it is i-stage substrate gaussian signal, and its dilatability is zi, symmetrical centre ui；
Step 5：By calculating maximum cross-correlation coefficient, the similitude of each substrate gaussian signal and input signal is acquired：
Wherein, rzi(τi) be input signal and i-stage substrate gaussian signal maximum cross-correlation value, τiFor two signal cross correlation values
When being maximized, the relative position of two signals is poor；
Step 6：Define artificial signal gf(x) it is the m substrate Gaussian functions with different dilatabilities and symmetrical centre, according to one
Signal after the fixed fitting weights superposition of group, and initial m=2 is set, fitting weights are one group of fixed constant, its choosing
It takes and follows dilatability ziIt is bigger, it is fitted this bigger principle of weights；
Step 7：According to the following formula, the artificial signal g being formed by stacking by one group of fixed fitting weights by substrate gaussian signal is calculatedf
(t),
Wherein φi(i∈[1,M]) it is fitting weights, it is one group of fixed constant,
Change the offset s of afterbody i.e. i-th (i=m) the grade substrate gaussian signal for forming this artificial signali, recalculate people
Signal is made, is further continued for calculating artificial signal and the cross correlation measure r of input signalf(τm)：
rf(τm)=max (cov<mN(t),gf(t)>)=max (∑ mN(τ)gf(t+τ))
Finding makes cross correlation measure rf(τm) it is maximum when offset si, it is s to add this offset at this timeiSubstrate gaussian signal when,
Artificial signal is the most similar to input signal, τmFor cross correlation measure maximum when artificial signal and position input signal it is poor；
Step 8：Make m=m+1, repeat step 7, until m=M, that is, added afterbody substrate Gaussian function；
Step 9：When the symmetrical centre offset of each substrate gaussian signal is respectively s1,s2,s3,…smWhen, by these substrates height
The artificial signal that this signal is composed has highest similarity with input signal, makes rfThe τ that (τ) is maximized is whole position
Difference is set, the optimal estimation p to central point is calculated：
Wherein λi(i∈[1,M]) be i-stage substrate gaussian signal symmetrical centre Combining weights, which is one group solid
Fixed constant, its selection follow dilatability ziIt is bigger, this bigger principle of Combining weights, uiFor pair of each substrate gaussian signal
Title center, rziThe maximum cross-correlation value of the substrate gaussian signal and input signal of the variant dilatability obtained for step 5, rzi
It is higher, then illustrate to include more dilatabilities in input signal to be ziSubstrate gaussian signal ingredient, ηiIt is normal to correct ratio
Number, the significance level of the symmetrical centre of the Gaussian function for reacting different dilatabilities；
Step 10：Step 2 is repeated to step 9, until all X have been calculatedmaxCapable central point, obtains to laser line image
The optimal estimation of center line.
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