CN109086795A - A kind of accurate elimination method of image mismatch - Google Patents
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Abstract
The present invention relates to a kind of accurate elimination methods of image mismatch, mesh fitting statistical restraint can quickly distinguish in the net region being sized it is correct matching to and erroneous matching pair, thus reject erroneous matching to retain correctly matching to obtain a rough matching collection；Rough matching can further be rejected to pole restricted model in conjunction with the improvement from the projection error function proposed and concentrate the matching pair for not meeting and improving to pole restricted model, to obtain good set of matches.This method merged mesh fitting statistical restraint can quickly distinguish in net region it is correct matching to and erroneous matching pair, and carry out reject and it is improved pole restricted model can be rejected quickly be unsatisfactory for pole constrain matching pair advantage, so as to obtain good matching pair, used time is less, and matching accuracy is higher.This method can adapt to the scene of different size complexity, and method is applied to visual synchronization and positions and build figure, and can all there be good effect in the fields such as three-dimensionalreconstruction and vision tracking.
Description
Technical field
The present invention relates to a kind of image processing techniques, in particular to a kind of fusion mesh fitting statistical restraint and improvement are to pole
The accurate elimination method of the image mismatch of restricted model.
Background technique
Images match is visual synchronization positioning and builds figure, and one of the fields such as three-dimensionalreconstruction and vision tracking is extremely important
The step of, it needs image matching algorithm while having real-time, accuracy and robustness.Image matching algorithm is divided into base
In the direct matching method of gray scale invariance and based on the matching algorithm of characteristic similarity.Feature-based matching method is generally logical
It crosses the local feature mapping relations found between two images to complete, including matching, line match and a region
Match.It is wherein based on points correspondence, since its extraction operation is simple, and matching way is flexible, the used time is less, therefore in image
It is commonly used in matching.
It is generally opened up after the SIFT algorithm based on scale invariability that Lowe [1] et al. is proposed based on matched work is put
Open, the algorithm is to dimensional variation, and rotation and illumination variation have good adaptability, but SIFT is computationally intensive, cost when
Between it is long, do not have real-time.The dimension that feature describes operator is reduced to 64 dimensions by Bay [2] et al., is proposed SURF matching and is calculated
Method although improving matching efficiency, but is positioned being used in mobile robot visual with navigation, and real-time is not much up to
It arrives.ORB (the Oriented FAST and Rotated for mostly using Rublee [3] et al. to propose in visual odometry greatly at present
BRIEF) Feature Correspondence Algorithm, ORB Feature Correspondence Algorithm are matched in feature point extraction with rapidity, are extracting sparse spy
Has preliminary real-time when sign point.But ORB matching algorithm has big when extraction obtains characteristic point and establishes initial matching
The Mismatching point of amount estimates inaccuracy so as to cause the pose of visual odometry, the problems such as poor robustness.
Therefore reject error hiding and retain it is good matching above is referred to field in be critically important part, exist at present
Rejecting error hiding traditional method is to obtain rough match point first with ORB algorithm, is then picked using RANSAC [4] algorithm
Except Mismatching point, but RANSAC algorithm has requirement to initial value, needs to be previously obtained preliminary accurate set of matches.Marius
Muja [5] et al. proposes that FLANN algorithm can be used to propose error matching points, obtains rough set of matches, but combine this method
Obtained match point is seldom, and quality of match is not high, is unfavorable for follow-up work.Xing Kaisheng [6] et al. is by combining ORB and RANSAC
Algorithm carries out error hiding deletion to ORB match point, but since RANSAC algorithm needs accurate initial value, and ORB algorithm is rigid
Matching obtained matching will lead to it and deletes to a large amount of error hiding pair, such RANSAC algorithm initial value inaccuracy is flooded with
Lead to very big inaccuracy when except error hiding.Qin Xiaofei [7] et al. rejects ORB error hiding by introducing epipolar-line constraint, but
The algorithm first has to reject Mismatching point using artificial given threshold, obtains thick set of matches, then epipolar-line constraint and RANSAC are calculated
Method, which combines, carries out error hiding rejecting.The algorithm needs artificial given threshold, has certain artifact when obtaining thick set of matches
Interference, and when training obtains threshold value, time cost is long, and algorithm is caused not have real-time.Bian Jiawang [8] et al. is mentioned
GMS (matching statistics) algorithm out quickly can reject error hiding pair to initial matching collection, not need artificial given threshold, but should
Algorithm can only obtain rough matching collection, can still there is error hiding, cannot obtain a large amount of good set of matches.
Summary of the invention
A large amount of error hidings can be led to the problem of the present invention be directed to current image matching algorithm and algorithm does not have real-time,
A kind of accurate elimination method of image mismatch is proposed, mesh fitting statistical restraint can quickly distinguish the net being sized
Correct matching in lattice region to and erroneous matching pair, so that it is thick to obtaining one to correctly matching is retained to reject erroneous matching
Slightly set of matches.Rough matching collection can further be rejected to pole restricted model in conjunction with the improvement from the projection error function proposed
In do not meet and improve matching pair to pole restricted model and improved to a certain extent matched to obtain good set of matches
Accuracy rate.
The technical solution of the present invention is as follows: a kind of accurate elimination method of image mismatch, specifically comprises the following steps:
1) two images with 10 °~15 ° angular transformations are shot to Same Scene with camera, by two images of acquisition
Input value as Feature Correspondence Algorithm；
2) to this two input pictures calculate separately ORB characteristic point and based on ORB characteristic point description son, and be based on the Chinese
Prescribed distance carries out simple matching, obtains initial matching pair；
3) mesh fitting statistical restraint is calculated, in the initial matching pair of acquisition to reject the matching for not meeting the constraint
It is right, obtain rough set of matches:
Assuming that thering is two frames are to be matched to obtain image Ia、Ib, X={ X1, X2..., XkIt is k initial matching pair, it first has to two
Width image Ia、IbIt is split with the grid being sized, the quantity of matching pair in each grid is then counted, to distinguish
The matching pair of right and wrong；
Define the matching statistical restraint Score index of every matching pair in any one gridCalculation formula are as follows:
The matching in this net region chosen is represented to quantity, subtracts 1 and is to remove itself this
Pairing；Define a scoring threshold calculations formula are as follows:α obtains value through experiment；Work as scoringWhen, then illustrate that this matching is otherwise erroneous matching pair correctly to being, to obtain a rough set of matches；
4) matching concentrated to rough matching in step 3) is ranked up from small to large to according to Hamming distance, is sorted
Then good set of matches M selects the first eight matching pair in M, to calculate to pole restricted model C, calculation formula is as follows:
r1Cr′1=0
r1=K-1p1
r′1=K-1p′1
K is camera internal reference, can be obtained by calibration；r1、r1' it is one group of characteristic point p matched1、p1After ' normalization
Coordinate, respectively r1=[u1, v1, 1], r1'=[u1', v1', 1], u1, v1It is characterized point p1Normalized image pixel coordinates,
u1', v1' it is characterized point p1' normalized image pixel coordinates；C is to have 9 known variables to pole restricted model, pass through following formula
Pole restricted model C is found out,
Then a projection error function is defined:
ds(C, pi, pi')=pi′-Cpi
Projection function extension:
pi' it is image IaIn arbitrary characteristics point piIt is projected in image IbIn some point on corresponding polar curve；With to pole about
Beam MODEL C combination projection error function PE obtain it is improved to pole restricted model PCM, then using improved to pole restricted model
PE-PCM goes the remaining matching pair in test set of matches M, if remaining matching is unsatisfactory for being adjusted to above-mentioned to the model is unsatisfactory for
Projection error function be greater than setting threshold value, then reject the matching pair；Otherwise, the matching pair for meeting the model is left, is obtained
Good set of matches.
The beneficial effects of the present invention are: the accurate elimination method of image mismatch of the present invention, this method have merged grid
With statistical restraint can quickly distinguish in net region it is correct matching to and erroneous matching to and rejected and improved
Pole restricted model can be rejected quickly be unsatisfactory for pole constrain matching pair advantage, so as to obtain good matching
Right, the used time is less, and matching accuracy is higher.And this method can adapt to the scene of different size complexity, in these scenes
There is good error hiding to reject effect.Our methods be applied to visual synchronization position with build figure, three-dimensionalreconstruction and vision with
Can all there be good effect in the fields such as track.
Detailed description of the invention
Fig. 1 is that the image mismatch of pole restricted model is accurately rejected in present invention fusion mesh fitting statistical restraint and improvement
The general flow chart of method；
Fig. 2 is two schematic diagrames of algorithm input value；
Fig. 3 is the Prototype drawing that ORB detects characteristic point；
Fig. 4 is the initial matching figure that ORB algorithm obtains；
Fig. 5 a is the effect picture that traditional algorithm rejects error hiding；
Fig. 5 b is that context of methods rejects error hiding effect picture.
Specific embodiment
The present invention is to obtain the input of algorithm by shooting two images with certain angle transformation with camera first
Value.Secondly, to this two input pictures calculate separately ORB characteristic point and based on ORB characteristic point description son, and be based on Hamming
Distance carries out simple matching, obtains initial matching pair.Again, mesh fitting statistical restraint is calculated in the initial matching pair of acquisition
(Grid-based Motion Statistics Constraint, GMSC) to reject some matchings pair for not meeting the constraint,
Obtain rough set of matches.Finally, by selecting the smallest eight pairs of matchings of Hamming distance to being calculated in rough matching concentration
To pole restricted model (Polar Constraint Model, PCM), then pole restricted model is combined from the projection error proposed
Function (Projection Error function, PE) obtain it is improved to pole restricted model (PE-PCM), with improved to pole
Restricted model goes to reject the matching pair for being unsatisfactory for the model, can obtain good matching pair.
The present embodiment we adopt and conform to the principle of simplicity with the aforedescribed process to several groups of Munich, Germany polytechnical university TUM data set to difficult
Image carries out test experiments, our method of result verification is suitble to the erroneous matching Weeding under any complex scene, energy
Good set of matches is enough obtained, more existing matching algorithm can be improved real-time.
It fusion mesh fitting statistical restraint and improves as shown in Figure 1 to the accurate side of rejecting of the image mismatch of pole restricted model
The general flow chart of method, includes the following steps:
The first step shoots two images with certain angle transformation to Same Scene with camera, obtains the input of algorithm
Value.Specifically: two images are shot to Same Scene at random using camera, but this two images need to have certain angle inclined
Turn, size can be 10 °~15 ° or so, then use this input value of two images as Feature Correspondence Algorithm.If Fig. 2 is TUM
Two pictures of middle rgbd_dataset_freiburg2_rpy data set have small angle change between them.
Second step calculates separately ORB characteristic point and description based on ORB characteristic point to this two input pictures, and
Simple matching is carried out based on Hamming distance, obtains initial matching pair.
Specifically: 1) ORB feature is calculated separately according to following steps in two images of input, selected on the image first
Take a candidate point p, the Prototype drawing of ORB detection characteristic point as shown in Figure 3, gray value Ip, then consider radius around point p
For the gray value of 3 upper 16 points of circle, feature points receptance function:
Circle (p) is the set of round upper 16 pixels, IqIt is the gray value at any point in set, θdIt is given for one
Fixed threshold value (such as desirable Ip20%).
N is finally calculated, N is that upper 16 pixels of circle and p point gray scale difference absolute value are greater than θdNumber, it is N number of continuous when having
Pixel when all meeting condition, then p is referred to as ORB characteristic point.
2) it is calculated separately after obtaining ORB characteristic point in two images, then calculates separately the ORB description of these characteristic points
Son, step are as follows: τ test is carried out in the neighborhood centered on first in two images characteristic point p, test function is
I (x), I (y) are respectively the gray value size of pixel x in two images in neighborhood, pixel y.
In two images by pass through near characteristic point p Gaussian Profile randomly select out m to test point carry out τ test,
Gaussian Profile is a distributed model in mathematics.The matrix for defining a 2 × m, is denoted as
(the x of each columni, yi) it is a pair of of the test point chosen, xiAnd yiRespectively for this to the corresponding pixel of test point.
It tests to obtain ORB finally by fixed τ and describes sub- g (p), fixed τ test is as follows:
3) the simple matching based on Hamming distance is carried out to obtained ORB description, Hamming distance indicates two identical length
The quantity of the character string corresponding position kinds of characters of degree.Son is described to ORB and carries out simple matching, when between two ORB description
Hamming distance be less than some threshold value, then be set to initial matching pair.
Third step calculates mesh fitting statistical restraint (Grid-based in the initial matching pair that previous step obtains
Motion Statistics Constraint, GMSC) to reject some matchings pair for not meeting the constraint, obtain rough
With collection.Such as Fig. 4 as can be seen that initial matching has a large amount of erroneous matching to presence, therefore you we need to use mesh fitting
Statistical restraint tentatively rejects erroneous matching pair.Mesh fitting statistical restraint concretism is: if from the difference of same 3D scene
A pair of of the image shot in view, if carrying out motion smoothing simultaneously, that is matched just in a small net region
True point pair is it can be seen that identical 3D scene, and matching error then sees different 3D scenes.And in the zonule, if
It is that correctly matching can be more to then surrounding number of matches, conversely, erroneous matching can less, therefore pass through rationally then surrounding quantity
The matching pair of right and wrong can then be distinguished to quantity by counting the matching in this net region.Specifically: assuming that having two
Frame is to be matched to obtain image Ia、Ib, X={ X1, X2..., XkIt is k initial matching pair, matching statistical restraint target is to distinguish
This k matching centering obtain correctly match to erroneous matching pair, and erroneous matching is rejected to as exterior point.It first has to two width figures
As Ia、IbIt is split with the grid being sized, then counts the quantity of matching pair in each grid, to distinguish correct
With the matching pair of mistake.Define the matching statistical restraint Score index of every matching pair in any one gridCalculation formula
Are as follows:
The matching in this net region chosen is represented to quantity, subtracts 1 and is to remove itself this
Pairing.Define a scoring threshold calculations formula are as follows:α can obtain value through experiment.Work as scoringWhen, then illustrate that this matching is otherwise erroneous matching pair correctly to being, to obtain a rough matching collection.
4th step selects the smallest eight pairs of matchings pair of Hamming distance by concentrating in rough matching, is calculated to pole about
Beam model (Polar Constraint Model, PCM) then combines from the projection error function proposed pole restricted model
(Projection Error function, PE) is obtained improved to pole restricted model (PE-PCM), is constrained with improved pole
Model goes to reject the matching pair for being unsatisfactory for the model, can obtain good matching pair, a degree of to improve matched standard
True rate.
Specifically: the matching concentrated to rough matching in third step is ranked up from small to large to according to Hamming distance, is obtained
Then the set of matches M that must have been sorted selects the first eight matching pair in M, to calculate to pole restricted model C, calculation formula is as follows:
r1Cr′1=0
r1=K-1p1
r′1=K-1p′1
K is camera internal reference, can be obtained by calibration.r1、r1' it is one group of characteristic point p matched1、p1After ' normalization
Coordinate, respectively r1=[u1, v1, 1], r1'=[u1', v1', 1], u1, v1It is characterized point p1Normalized image pixel coordinates,
u1', v1' it is characterized point p1' normalized image pixel coordinates.C is to have 9 known variables, due to above formula to pole restricted model
All it is of equal value under different scale, therefore lacks a scale factor, need to can be only found out with eight pairs of points in set of matches pair
Pole restricted model C.
It can be found out in this way to pole restricted model C, then define a projection error function:
ds(C, pi, pi')=pi′-Cpi
Projection function extension:
pi' for the arbitrary characteristics point p in Fig. 4 left imageiIt is projected in some point in right image on corresponding polar curve.With right
Pole restricted model C combination projection error function (PE) obtain it is improved to pole restricted model (PCM), then using improved to pole
Restricted model (PE-PCM) goes the remaining matching pair in test set of matches M, if remaining matching is to being unsatisfactory for the model, i.e., above-mentioned
Projection error function be greater than certain threshold value, then reject the matching pair.Otherwise, the matching pair for meeting the model is left, is obtained
Good set of matches.
Fig. 5 a, 5b are that several groups of pictures that we pick in TUM data set are tested, and Fig. 5 a is that traditional algorithm rejects mistake
Matched effect picture, Fig. 5 b are that context of methods rejects error hiding effect picture, this several groups of picture match difficulties are conformed to the principle of simplicity respectively to difficulty, can
To find out that we have good error hiding to reject effect various scenes at method.From when the matching of several algorithms of different of the following table 1
Between in comparing result as can be seen that we are fewer than the matching algorithm used time of traditional matching algorithm and several classics the method used time,
Real-time is higher.Traditional algorithm herein is to obtain initial matching using ORB algorithm, then using FLANN algorithm and
RANSAC algorithm, which combines, carries out wrong rejecting.SURF, SIFT are the image matching algorithms of several classics, are calculated also with FLANN
Method and RANSAC algorithm, which combine, carries out wrong rejecting.Then total used time used in them is calculated, is compared with our methods.
Table 1
Claims (1)
1. a kind of accurate elimination method of image mismatch, which is characterized in that specifically comprise the following steps:
1) two images with 10 °~15 ° angular transformations are shot to Same Scene with camera, using two images of acquisition as
The input value of Feature Correspondence Algorithm；
2) to this two input pictures calculate separately ORB characteristic point and based on ORB characteristic point description son, and be based on Hamming distance
From simple matching is carried out, initial matching pair is obtained；
3) mesh fitting statistical restraint is calculated in the initial matching pair of acquisition, to reject the matching pair for not meeting the constraint, is obtained
To rough set of matches:
Assuming that thering is two frames are to be matched to obtain image Ia、Ib, X={ X1, X2..., XkIt is k initial matching pair, it first has to two width figures
As Ia、IbIt is split with the grid being sized, then counts the quantity of matching pair in each grid, to distinguish correct
With the matching pair of mistake；
Define the matching statistical restraint Score index of every matching pair in any one gridCalculation formula are as follows:
The matching in this net region chosen is represented to quantity, subtracts 1 and be this matching in order to remove itself
It is right；Define a scoring threshold calculations formula are as follows:α obtains value through experiment；Work as scoring
When, then illustrate that this matching is otherwise erroneous matching pair correctly to being, to obtain a rough set of matches；
4) matching concentrated to rough matching in step 3) is ranked up from small to large to according to Hamming distance, what acquisition had been sorted
Then set of matches M selects the first eight matching pair in M, to calculate to pole restricted model C, calculation formula is as follows:
r1Cr′1=0
r1=K-1p1
r′1=K-1p′1
K is camera internal reference, can be obtained by calibration；r1、r1' it is one group of characteristic point p matched1、p1Seat after ' normalization
Mark, respectively r1=[u1, v1, 1], r1'=[u1', v1', 1], u1, v1It is characterized point p1Normalized image pixel coordinates, u1',
v1' it is characterized point p1' normalized image pixel coordinates；C is to have 9 known variables to pole restricted model, found out by following formula
Pole restricted model C,
Then a projection error function is defined:
ds(C, pi, pi')=pi′-Cpi
Projection function extension:
pi' it is image IaIn arbitrary characteristics point piIt is projected in image IbIn some point on corresponding polar curve；Mould is constrained with to pole
Type C combination projection error function PE obtain it is improved to pole restricted model PCM, then using improved to pole restricted model PE-
PCM goes the remaining matching pair in test set of matches M, if remaining matching is unsatisfactory for being adjusted to above-mentioned to the model is unsatisfactory for
Projection error function is greater than the threshold value of setting, then rejects the matching pair；Otherwise, the matching pair for meeting the model is left, is obtained excellent
The set of matches of matter.
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