CN107993193A - The tunnel-liner image split-joint method of surf algorithms is equalized and improved based on illumination - Google Patents

The tunnel-liner image split-joint method of surf algorithms is equalized and improved based on illumination Download PDF

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CN107993193A
CN107993193A CN201710857638.0A CN201710857638A CN107993193A CN 107993193 A CN107993193 A CN 107993193A CN 201710857638 A CN201710857638 A CN 201710857638A CN 107993193 A CN107993193 A CN 107993193A
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mtd
image
point
illumination
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CN107993193B (en
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薛丹
王新宇
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Shenyang University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/10—Image enhancement or restoration using non-spatial domain filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details

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Abstract

The tunnel-liner image split-joint method of surf algorithms is equalized and improved based on illumination, and this method includes:（1）, fitting illumination curve distribution algorithm tunnel picture is handled, prominent features and the influence for reducing uneven illumination；（2）, using sruf Feature Correspondence Algorithms find sruf features, and the above-mentioned characteristic point detected is matched using FlannBasedMatcher adaptations；（3）, using symmetry matching strategy, distance matching limits algorithm, and to epipolar-line constraint the methods of carries out matching constraint；（4）, ask for using mathematical way the desired distance of image, image is subjected to segmentation splicing by image.While the present invention can remove high light, retain edge of crack details.Separated tunnel each several part can be connected into an entirety, can accurately detect the length in longer crack, and a secondary more complete tunnel plate image is provided, facilitate the accurate positionin of various problems.

Description

The tunnel-liner image split-joint method of surf algorithms is equalized and improved based on illumination
Technical field：
The invention belongs to technical field of computer vision, more particularly to a kind of tunnel-liner merging algorithm for images.
Background technology：
Shielding force at direction of pump spindle whether balance influence to canned motor pump can safe and reliable operation.Axial force is by thrust disc with sliding The axial end face friction pair of bearing undertakes, and axial force is excessive, causes bearing wear to aggravate, and axial gap becomes larger, and makes shielding pump shaft The service life held rapidly reduces, even result in electric pump can not normal operation, cause the accident.Machine halt trouble occurs for canned motor pump, greatly Partly cause is all due to that axial force is not balanced effectively or graphite bearing damages.It is thus accurate to shielding force at direction of pump spindle Really, it is efficient to calculate, and be effectively balanced axial force, service life and safe and reliable fortune for canned motor pump Row has great significance.
The content of the invention：
Goal of the invention：
The present invention provides a kind of tunnel-liner image split-joint method for being equalized and being improved surf algorithms based on illumination, its mesh Be solve in the past the problems of.
Technical solution：
A kind of tunnel-liner image split-joint method for equalizing and improving surf algorithms based on illumination, it is characterised in that：Should Method includes:
(1), the algorithm for being fitted illumination curve distribution is handled tunnel picture, and prominent features simultaneously reduce uneven illumination Influence；
(2), sruf features are found using sruf Feature Correspondence Algorithms, and will using FlannBasedMatcher adaptations The above-mentioned characteristic point detected is matched；
(3), using symmetry matching strategy, distance matching limits algorithm, and to epipolar-line constraint the methods of carries out matching constraint；
(4), the desired distance of image is asked for using mathematical way, image is subjected to segmentation splicing by image.
(1) in step, the algorithm steps for reducing the i.e. balanced uneven illumination of influence of uneven illumination are as follows：
Improved mean filter denoising, gaussian pyramid compression denoising and improved gaussian filtering fitting illumination curve point Cloth.
(3) characteristic matching limitation uses following three kinds of schemes in step：
Symmetry matching strategy, distance matching limit algorithm and stochastical sampling consistency algorithm.
This method step is as follows：
(1) algorithm for being fitted illumination curve distribution is handled tunnel picture, and prominent features simultaneously reduce uneven illumination Influence；
(1.1) improved mean filter denoising：
To remove spuious bright noise, mean filter is carried out to original image using (2*t+1) * (2*t+1) templates, by average Filtered image takes the minimum value of each respective pixel to form new image compared with each grey scale pixel value of original image； Retain FRACTURE CHARACTERISTICS while removing high bright；
G (i, j)=min [s (i, j), r (i, j)]
Wherein n=m=45；I, j are defined in the transverse and longitudinal coordinate on image, and s (i, j) is that original image coordinate is (i, j) place Grey scale pixel value, r (i, j) is the grey scale pixel value that image coordinate after mean filter is (i, j) place, and g (i, j) is equal to improve Image coordinate after value filtering processing is the grey scale pixel value at (i, j) place；
(1.2), gaussian pyramid compression denoising：
Compression of images is carried out using the image pyramid compress mode of some resolution ratio multi-scale expressions；Different levels correspond to The image of different resolution；Image material particular is not lost as far as possible while noise is removed, and can improve computing speed Degree, while the gradient in crack and background is retained, eliminates small echo moving noise；
Wherein, by the way that to input original image i stackings, for above step, obtained image is i+1 layers of image.w(m, N) it is Gaussian convolution core, gl-1(i, j) is the grey scale pixel value that the i-th tomographic image coordinate is (i, j) place, gl(i, j) is i+1 layer Image coordinate is the grey scale pixel value at (i, j) place.
(1.3), improved gaussian filtering fitting illumination curve distribution：
Illumination curve distribution is fitted, is smoothed equivalent to gray scale section curve figure, by section and one Gaussian filter carries out convolution to realize；
X (i, j)=g (i, j) * h (i, j)
X (i, j) is to do convolution with Gaussian filter h (i, j) and image g (i, j) to estimate optical field distribution situation；
Make o (i, j)=t (i, j)-x (i, j)
Wherein i, j are defined in the transverse and longitudinal coordinate on image, and t (i, j) is that image coordinate is (i, j) place after pyramidal compression Grey scale pixel value, x (i, j) is the grey scale pixel value that image coordinate is (i, j) place after gaussian filtering, and o (i, j) is removes illumination Image coordinate is the grey scale pixel value at (i, j) place after curved surface, then compared with the image average after removing illumination curved surface, is retained Less than the pixel value of equal value part, so as to filter out noise, y (i, j) is the pixel grey scale that image coordinate is (i, j) place after pre-processing Value；
(2), sruf features are found using sruf Feature Correspondence Algorithms, and will using FlannBasedMatcher adaptations The above-mentioned characteristic point detected is matched：
(2.1), SurfFeatureDetector property detectors are created characteristic point detection is carried out to image, and will detection Characteristic point out is stored in characteristic point array keypoints_1, keypoints_2 respectively；
(2.2), structure SurfDescriptorExtractor profilers describe SURF features；
(2.3), the use of FlannBasedMatcher adaptations is the descriptor based on scalable nearest neighbor algorithm Flann Adaptation is matched the above-mentioned characteristic point detected；
(3), three kinds of schemes of characteristic matching limitation：
(3.1), symmetry matching strategy：Remove unstable match point；
(3.2), distance matching limits algorithm：
The particular content of algorithm is the speed v and camera frame per second fps obtained by outside, then time downlink of the car in a frame Into distance be exactly s=v* (1/fps), due to wanting vehicle approximation to remain a constant speed movement, then garage into distance s and same position The location of pixels difference x put on the image is the relation mapped one by one, we set this coefficient of relationship as k, then x=ks, due to by mistake The presence of difference, the neighborhood section (x ± δ) that we set an x is defined as threshold interval；
(3.3), stochastical sampling consistency algorithm (RANSAC)：
RANSAC estimates the ginseng of mathematical model by iterative manner from one group of observation data set comprising " point not in the know " Number；It is a kind of uncertain algorithm --- it has certain probability to draw a rational result；It is necessary in order to improve probability Improve iterations；
(4), image is carried out segmentation splicing by the relative distance that image is asked for using mathematical way, and algorithm thinking is：Calculate The slope of line between each match pointThe mode of the most match point, that is, slope of slope is chosen, these matchings Point is exactly correct；
(3.1) in step, remove unstable match point algorithm realizes that process is as follows：
1) index of each match point in above-mentioned matching is found；
2) judge that the match point under the relative indexing in matching twice is opposite；
3) corresponding point in matching twice is preserved；
4) a each call number of match point is preserved.
(3.2) in step, which realizes that process is as follows：
1) with secondary each opposite match point of traversal, and the lateral separation between two match points is calculated；
2) limited using threshold value, the cycle through taking pictures, the travel speed of car also have the distance three of vehicle distances wall into Row the Fitting Calculation goes out suitable distance threshold (x ± δ).
(3.3) in step, the basic assumption of RANSAC is：
1) data are made of " intra-office point "；
2) " point not in the know " is the data for not adapting to the model；
3) data in addition belong to noise；
Not in the know Producing reason has：The extreme value of noise；The measuring method of mistake；To the false supposition of data；
RANSAC reaches target by one group of random subset being chosen in data；The subset being selected is assumed to be Intra-office point, and verified with following methods：
1) there is the intra-office point that a model is adapted to hypothesis, i.e., all unknown parameters can be calculated from the intra-office point of hypothesis Draw；
2) gone to test all other data with the model obtained in 1, if some point is suitable for the model of estimation, it is believed that It is also intra-office point；
3) if enough points are classified as the intra-office point of hypothesis, then the model of estimation is just reasonable enough；
4) then, go to reevaluate model with the intra-office point of all hypothesis, because it is only by initial hypothesis intra-office point Estimated；
5) finally, by estimating the error rate of intra-office point and model come assessment models；
This process is repeatedly executed fixed number, otherwise the model produced every time is because intra-office point is given up very little Abandon, otherwise it is selected because of more preferable than existing model；The algorithm realizes process：
1) characteristic point keypoints, is changed into the points types of coordinate form by transformation matrix；
1) basis matrix of image is calculated；
2) screening of exterior point in carrying out, point in preservation, eliminates exterior point.
(4) process of realizing of step is：
(4.1) find characteristic point above and matched；
(4.2) the distance between characteristic point among calculating two images；
(4.3) obtained distance is planned using the mode in mathematical algorithm；
(4.4) image among the second width image is translated；
(4.5) image after processing is spliced.
Advantageous effect：
The present invention provides a kind of tunnel-liner image split-joint method for being equalized and being improved surf algorithms based on illumination, this hair It is bright while can remove high light, retain edge of crack details.Separated tunnel each several part can be connected into an entirety, The length in longer crack can be accurately detected, and a secondary more complete tunnel plate image is provided, facilitates the accurate positionin of various problems.
Brief description of the drawings：
Fig. 1 a are artwork；
Fig. 1 b are section curve figure；
Fig. 1 c are pretreatment image；
Fig. 1 d scheme for matching；
Fig. 1 e are spliced map.
Embodiment：
The present invention relates to a kind of tunnel-liner image split-joint method for equalizing and improving surf algorithms based on illumination, such as scheme Shown in 1, this method includes：It is proposed that a kind of algorithm for being fitted illumination curve distribution is handled tunnel picture, prominent features are simultaneously Reduce the influence of uneven illumination；Sruf features are found using sruf Feature Correspondence Algorithms, and use FlannBasedMatcher Orchestration is matched the above-mentioned characteristic point detected；Using symmetry matching strategy, distance matching limits algorithm, to polar curve about The methods of beam, carries out matching constraint；The desired distance of image is asked for using mathematical way, image is subjected to segmentation splicing by image.
Comprise the following steps that：
1. shown in the original image figure (a) gathered in tunnel, wherein a certain row are chosen, is drawn and cutd open according to grey scale pixel value Surface curve figure, such as schemes shown in (b), it is seen that not only have the Curvature Effect of illumination curve distribution, also containing substantial amounts of noise.Therefore examine Worry first pre-processes image, proposes a kind of algorithm of balanced uneven illumination.Algorithm realizes process.
1.1 improved mean filter denoisings
The characteristics of what is presented in the picture due to crack is to have low gray value, and high gradient is poor.Therefore, higher than equal in image The gray value of value must be noise, to remove this spuious bright noise, by the image after mean filter and each pixel of original image Gray value is compared, and takes the minimum value of each respective pixel to form new image.Because the gray value in crack is relatively low, illumination ash Angle value is higher, and institute while removing high bright so as to retain FRACTURE CHARACTERISTICS.
G (i, j)=min [s (i, j), r (i, j)]
Wherein n=m=45.I, j are defined in the transverse and longitudinal coordinate on image, and s (i, j) is that original image coordinate is (i, j) place Grey scale pixel value, r (i, j) is the grey scale pixel value that image coordinate after mean filter is (i, j) place, and g (i, j) is equal to improve Image coordinate after value filtering processing is the grey scale pixel value at (i, j) place.
1.2 gaussian pyramids compress denoising
Due to crack, to have width to have narrow, the gradient difference contrast between crack and background have by force have it is weak, therefore, using some points The image pyramid compress mode of resolution multi-scale expression carries out compression of images.Different levels correspond to the image of different resolution. This method does not lose image material particular as far as possible while noise is removed, and can improve arithmetic speed, is split in reservation While seam and the gradient of background, small echo moving noise is eliminated.
1.3 improved gaussian filtering fitting illumination curve distributions
Illumination curve distribution is fitted, is smoothed equivalent to gray scale section curve figure, this can be by section Convolution is carried out with a Gaussian filter to realize.Illumination patterns uniformization effect after the algorithm process is good.Pre- place Image after reason is as schemed shown in (c).
X (i, j)=g (i, j) * h (i, j)
X (i, j) is to do convolution with Gaussian filter h (i, j) and image g (i, j) to estimate optical field distribution situation.
Make o (i, j)=t (i, j)-x (i, j)
Wherein i, j are defined in the transverse and longitudinal coordinate on image, and t (i, j) is that image coordinate is (i, j) place after pyramidal compression Grey scale pixel value, x (i, j) is the grey scale pixel value that image coordinate is (i, j) place after gaussian filtering, and o (i, j) is removes illumination Image coordinate is the grey scale pixel value at (i, j) place after curved surface, then compared with the image average after removing illumination curved surface, is retained Less than the pixel value of equal value part, so as to filter out noise, y (i, j) is the pixel grey scale that image coordinate is (i, j) place after pre-processing Value.
2. sruf features are found using sruf Feature Correspondence Algorithms, and will be upper using FlannBasedMatcher adaptations The characteristic point detected is stated to be matched
2.1 create SurfFeatureDetector property detectors, and (SURFFeatureDetector is in OpenCV2 Generic features detector introduces a kind of new general-purpose interface and is used for different detectors.It this interface define a KeyPoint Class is to encapsulate the attribute of each characteristic point.For Harris angle points, only position is useful), characteristic point inspection is carried out to image Survey, and the characteristic point detected is stored in characteristic point array keypoints_1, keypoints_2 respectively.
2.2 structure SurfDescriptorExtractor profilers (are equally the generic features inspections in OpenCV 2 Survey device and introduce a kind of new general-purpose interface, carry out feature description), SURF features are described.
2.3 use the FlannBasedMatcher adaptation (descriptors match based on scalable nearest neighbor algorithm Flann Device) the above-mentioned characteristic point detected is matched.
3. three kinds of schemes of characteristic matching limitation
3.1 symmetry matching strategies
The principle of above-mentioned Feature Points Matching is gone and the characteristic point on another piece image with the characteristic point on piece image Matching, finds the characteristic point of matching probability maximum as match point, we guess, the second width figure of matching is removed by piece image Obtained match point should with by the second width figure go the obtained match point of the first width figure of matching should be it is consistent, it is still, actual On twice matched result be not consistent, so according to this consistent principle above, those unstable can be removed With point, which realizes process：
5) index of each match point in above-mentioned matching is found.
6) judge that the match point under the relative indexing in matching twice is opposite.
7) corresponding point in matching twice is preserved.
8) a each call number of match point is preserved.
3.2 distance matchings limit algorithm
The tunneling features point that narrow sense is changed to by the limitation of extensive Feature Points Matching matches limitation, and then we think, according to car Contact between speed and the frame per second taken pictures, estimates a shift length on image, is limited afterwards into row distance, it should Matched error rate can be reduced.The particular content of algorithm is the speed v and camera frame per second fps obtained by outside, then car exists The distance advanced under the time of one frame is exactly s=v* (1/fps), due to wanting vehicle approximation to remain a constant speed movement, then garage into Distance s and the location of pixels difference x of same position on the image be the relation mapped one by one, we set this coefficient of relationship as k, Then x=ks, due to the presence of error, the neighborhood section (x ± δ) that we set an x is defined as threshold interval.The calculation Method realizes process：
1) with secondary each opposite match point of traversal, and the lateral separation between two match points is calculated.
2) limited using threshold value, the cycle through taking pictures, the travel speed of car also have the distance three of vehicle distances wall into Row the Fitting Calculation goes out suitable distance threshold (x ± δ).
3.3 stochastical sampling consistency algorithms (RANSAC)
RANSAC can estimate mathematical model from one group of observation data set comprising " point not in the know " by iterative manner Parameter.It is a kind of uncertain algorithm --- it has certain probability to draw a rational result；Must in order to improve probability Iterations must be improved.
The basic assumption of RANSAC is：
2) data are made of " intra-office point ", such as：The distribution of data can be explained with some model parameters；
2) " point not in the know " is the data for not adapting to the model；
3) data in addition belong to noise.
Not in the know Producing reason has：The extreme value of noise；The measuring method of mistake；To the false supposition of data.
RANSAC reaches target by one group of random subset being chosen in data.The subset being selected is assumed to be Intra-office point, and verified with following methods：
1) there is the intra-office point that a model is adapted to hypothesis, i.e., all unknown parameters can be calculated from the intra-office point of hypothesis Draw.
2) gone to test all other data with the model obtained in 1, if some point is suitable for the model of estimation, it is believed that It is also intra-office point.
3) if enough points are classified as the intra-office point of hypothesis, then the model of estimation is just reasonable enough.
4) then, go to reevaluate model with the intra-office point of all hypothesis, because it is only by initial hypothesis intra-office point Estimated.
5) finally, by estimating the error rate of intra-office point and model come assessment models.
This process is repeatedly executed fixed number, otherwise the model produced every time is because intra-office point is given up very little Abandon, otherwise it is selected because of more preferable than existing model.The algorithm realizes process：
1) characteristic point keypoints, is changed into the points types of coordinate form by transformation matrix.
3) basis matrix of image is calculated.
4) screening of exterior point in carrying out, point in preservation, eliminates exterior point.
Image result after matching is as schemed (d).
4. asking for the relative distance of image using mathematical way, image is subjected to segmentation splicing by image, algorithm thinking is： Since the probability that error hiding occurs in matching process is very high, but for substantial amounts of matched data, this point point is very It is small, in order to increase accuracy, reduce the influence of mistake, we calculate the slope of line between each match point here(note：Opposite characteristic point is respectively (x on two width figuresi,yi) and (xj,yj)) due to correct match point herein it Between line it is always parallel, and mistake match point between slope it is widely different since error connection line is seldom, we select The most match point of the slope i.e. mode of slope is taken, these match points are exactly correct.The process of realization is：
4.1 find characteristic point above and are matched.
The distance between characteristic point among 4.2 calculating two images.
4.3 are planned obtained distance using the mode in mathematical algorithm
4.4 are translated the image among the second width image
4.5 are spliced the image after processing
Spliced image effect is as schemed (e).

Claims (8)

  1. A kind of 1. tunnel-liner image split-joint method for equalizing and improving surf algorithms based on illumination, it is characterised in that：The party Method includes:
    (1), the algorithm for being fitted illumination curve distribution is handled tunnel picture, prominent features and the shadow for reducing uneven illumination Ring；
    (2), sruf features are found using sruf Feature Correspondence Algorithms, and will be above-mentioned using FlannBasedMatcher adaptations The characteristic point detected is matched；
    (3), using symmetry matching strategy, distance matching limits algorithm, and to epipolar-line constraint the methods of carries out matching constraint；
    (4), the desired distance of image is asked for using mathematical way, image is subjected to segmentation splicing by image.
  2. 2. the tunnel-liner image split-joint method according to claim 1 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    (1) in step, the algorithm steps for reducing the i.e. balanced uneven illumination of influence of uneven illumination are as follows：
    Improved mean filter denoising, gaussian pyramid compression denoising and improved gaussian filtering fitting illumination curve distribution.
  3. 3. the tunnel-liner image split-joint method according to claim 1 for equalizing and improving surf algorithms based on illumination, It is characterized in that：(3) characteristic matching limitation uses following three kinds of schemes in step：
    Symmetry matching strategy, distance matching limit algorithm and stochastical sampling consistency algorithm.
  4. 4. the tunnel-liner image split-joint method according to claim 1 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    This method step is as follows：
    (1) algorithm for being fitted illumination curve distribution is handled tunnel picture, prominent features and the influence for reducing uneven illumination；
    (1.1) improved mean filter denoising：
    To remove spuious bright noise, mean filter is carried out to original image using (2*t+1) * (2*t+1) templates, by mean filter Image afterwards takes the minimum value of each respective pixel to form new image compared with each grey scale pixel value of original image；Going Retain FRACTURE CHARACTERISTICS while except high light；
    <mrow> <mi>r</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mo>(</mo> <mn>2</mn> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> <mo>*</mo> <mo>(</mo> <mn>2</mn> <mi>m</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mo>-</mo> <mi>n</mi> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mo>-</mo> <mi>m</mi> </mrow> <mi>m</mi> </munderover> <mi>s</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mi>t</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow>
    G (i, j)=min [s (i, j), r (i, j)]
    Wherein n=m=45；I, j are defined in the transverse and longitudinal coordinate on image, and s (i, j) is the picture that original image coordinate is (i, j) place Plain gray value, r (i, j) are that the image coordinate after mean filter is the grey scale pixel value at (i, j) place, and g (i, j) is improvement average filter Image coordinate after ripple processing is the grey scale pixel value at (i, j) place；
    (1.2), gaussian pyramid compression denoising：
    Compression of images is carried out using the image pyramid compress mode of some resolution ratio multi-scale expressions；Different levels correspond to different The image of resolution ratio；Image material particular is not lost as far as possible while noise is removed, and can improve arithmetic speed, While the gradient of reservation crack and background, small echo moving noise is eliminated；
    <mrow> <mi>w</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>256</mn> </mfrac> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>4</mn> </mtd> <mtd> <mn>6</mn> </mtd> <mtd> <mn>4</mn> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> <mtr> <mtd> <mn>4</mn> </mtd> <mtd> <mn>16</mn> </mtd> <mtd> <mn>24</mn> </mtd> <mtd> <mn>16</mn> </mtd> <mtd> <mn>4</mn> </mtd> </mtr> <mtr> <mtd> <mn>6</mn> </mtd> <mtd> <mn>24</mn> </mtd> <mtd> <mn>36</mn> </mtd> <mtd> <mn>24</mn> </mtd> <mtd> <mn>6</mn> </mtd> </mtr> <mtr> <mtd> <mn>4</mn> </mtd> <mtd> <mn>16</mn> </mtd> <mtd> <mn>24</mn> </mtd> <mtd> <mn>16</mn> </mtd> <mtd> <mn>4</mn> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>4</mn> </mtd> <mtd> <mn>6</mn> </mtd> <mtd> <mn>4</mn> </mtd> <mtd> <mn>1</mn> </mtd> </mtr> </mtable> </mfenced> </mrow>
    <mrow> <msub> <mi>g</mi> <mi>l</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mn>1</mn> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mn>1</mn> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <mi>w</mi> <mrow> <mo>(</mo> <mi>m</mi> <mn>1</mn> <mo>,</mo> <mi>n</mi> <mn>1</mn> <mo>)</mo> </mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <mi>w</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mo>-</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>&amp;times;</mo> <mo>(</mo> <mrow> <mn>2</mn> <mi>i</mi> <mo>+</mo> <mi>m</mi> <mn>1</mn> </mrow> <mo>)</mo> <mo>+</mo> <mi>m</mi> <mo>,</mo> <mn>2</mn> <mo>&amp;times;</mo> <mo>(</mo> <mrow> <mn>2</mn> <mi>j</mi> <mo>+</mo> <mi>n</mi> <mn>1</mn> </mrow> <mo>)</mo> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>g</mi> <mi>l</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> </mrow> <mn>2</mn> </munderover> <mi>w</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <msub> <mi>g</mi> <mrow> <mi>l</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mi>i</mi> <mo>+</mo> <mi>m</mi> <mo>,</mo> <mn>2</mn> <mi>j</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow>
    Wherein, by the way that to input original image i stackings, for above step, obtained image is i+1 layers of image；W (m, n) is Gaussian convolution core, gl-1(i, j) is the grey scale pixel value that the i-th tomographic image coordinate is (i, j) place, gl(i, j) is i+1 tomographic image Coordinate is the grey scale pixel value at (i, j) place；
    (1.3), improved gaussian filtering fitting illumination curve distribution：
    Illumination curve distribution is fitted, is smoothed equivalent to gray scale section curve figure, by section and a Gauss Smoothing filter carries out convolution to realize；
    <mrow> <mi>h</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msup> <mi>&amp;pi;&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <msup> <mi>i</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mi>j</mi> <mn>2</mn> </msup> <mo>)</mo> </mrow> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> </msup> </mrow>
    X (i, j)=g (i, j) * h (i, j)
    X (i, j) is to do convolution with Gaussian filter h (i, j) and image g (i, j) to estimate optical field distribution situation；
    Make o (i, j)=t (i, j)-x (i, j)
    <mrow> <mi>y</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>min</mi> <mo>&amp;lsqb;</mo> <mfrac> <mn>1</mn> <mrow> <mo>(</mo> <mn>2</mn> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> <mo>*</mo> <mo>(</mo> <mn>2</mn> <mi>m</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mo>-</mo> <mi>n</mi> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mo>-</mo> <mi>m</mi> </mrow> <mi>m</mi> </munderover> <mi>o</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mi>t</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>o</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow>
    Wherein i, j are defined in the transverse and longitudinal coordinate on image, and t (i, j) is the picture that image coordinate is (i, j) place after pyramidal compression Plain gray value, x (i, j) are the grey scale pixel value that image coordinate is (i, j) place after gaussian filtering, and o (i, j) is removes illumination curved surface Image coordinate is the grey scale pixel value at (i, j) place afterwards, then compared with the image average after removing illumination curved surface, reservation is less than The pixel value of equal value part, so as to filter out noise, y (i, j) is the grey scale pixel value that image coordinate is (i, j) place after pre-processing；
    (2), sruf features are found using sruf Feature Correspondence Algorithms, and will be above-mentioned using FlannBasedMatcher adaptations The characteristic point detected is matched：
    (2.1), SurfFeatureDetector property detectors are created and characteristic point detection is carried out to image, and will detected Characteristic point be stored in respectively in characteristic point array keypoints_1, keypoints_2；
    (2.2), structure SurfDescriptorExtractor profilers describe SURF features；
    (2.3), the use of FlannBasedMatcher adaptations is the descriptors match based on scalable nearest neighbor algorithm Flann Device is matched the above-mentioned characteristic point detected；
    (3), three kinds of schemes of characteristic matching limitation：
    (3.1), symmetry matching strategy：Remove unstable match point；
    (3.2), distance matching limits algorithm：
    The particular content of algorithm is the speed v and camera frame per second fps obtained by outside, then car is advanced under the time of a frame Distance is exactly s=v* (1/fps), due to want vehicle approximation to remain a constant speed movement, then garage into distance s and same position exist Location of pixels difference x on image is the relation mapped one by one, we set this coefficient of relationship as k, then x=ks, due to error In the presence of the neighborhood section (x ± δ) that we set an x is defined as threshold interval；
    (3.3), stochastical sampling consistency algorithm (RANSAC)：
    RANSAC estimates the parameter of mathematical model by iterative manner from one group of observation data set comprising " point not in the know "；It It is a kind of uncertain algorithm --- it has certain probability to draw a rational result；It must be improved repeatedly to improve probability Generation number；
    (4), image is carried out segmentation splicing by the relative distance that image is asked for using mathematical way, and algorithm thinking is：Calculate each The slope of line between bar match pointThe mode of the most match point, that is, slope of slope is chosen, these match points are just It is correct；
  5. 5. the tunnel-liner image split-joint method according to claim 4 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    (3.1) in step, remove unstable match point algorithm realizes that process is as follows：
    1) index of each match point in above-mentioned matching is found；
    2) judge that the match point under the relative indexing in matching twice is opposite；
    3) corresponding point in matching twice is preserved；
    4) a each call number of match point is preserved.
  6. 6. the tunnel-liner image split-joint method according to claim 4 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    (3.2) in step, which realizes that process is as follows：
    1) with secondary each opposite match point of traversal, and the lateral separation between two match points is calculated；
    2) limited using threshold value, the cycle through taking pictures, the distance three that the travel speed of car also has vehicle distances wall is intended It is total to calculate suitable distance threshold (x ± δ).
  7. 7. the tunnel-liner image split-joint method according to claim 4 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    (3.3) in step, the basic assumption of RANSAC is：
    1) data are made of " intra-office point "；
    2) " point not in the know " is the data for not adapting to the model；
    3) data in addition belong to noise；
    Not in the know Producing reason has：The extreme value of noise；The measuring method of mistake；To the false supposition of data；
    RANSAC reaches target by one group of random subset being chosen in data；The subset being selected is assumed to be intra-office Point, and verified with following methods：
    1) there is the intra-office point that a model is adapted to hypothesis, i.e., all unknown parameters can be calculated from the intra-office point of hypothesis Go out；
    2) gone to test all other data with the model obtained in 1, if some point is suitable for the model of estimation, it is believed that it It is intra-office point；
    3) if enough points are classified as the intra-office point of hypothesis, then the model of estimation is just reasonable enough；
    4) then, go to reevaluate model with the intra-office point of all hypothesis, because it is only by initial hypothesis intra-office point estimation Cross；
    5) finally, by estimating the error rate of intra-office point and model come assessment models；
    This process is repeatedly executed fixed number, otherwise the model produced every time is wanted because intra-office point is rejected very little It is selected because of more preferable than existing model；The algorithm realizes process：
    1) characteristic point keypoints, is changed into the points types of coordinate form by transformation matrix；
    1) basis matrix of image is calculated；
    2) screening of exterior point in carrying out, point in preservation, eliminates exterior point.
  8. 8. the tunnel-liner image split-joint method according to claim 4 for equalizing and improving surf algorithms based on illumination, It is characterized in that：
    (4) process of realizing of step is：
    (4.1) find characteristic point above and matched；
    (4.2) the distance between characteristic point among calculating two images；
    (4.3) obtained distance is planned using the mode in mathematical algorithm；
    (4.4) image among the second width image is translated；
    (4.5) image after processing is spliced.
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