CN104077769A - Error matching point pair removing algorithm in image registration - Google Patents
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Abstract
The invention discloses an error matching point pair removing algorithm in image registration. The method comprises the following steps that firstly, a feature detection and description operator with the dimension invariance and the rotation invariance is used for finding out a feature point of an image and obtaining a descriptor; secondly, a k-d tree(k-dimensional tree) algorithm is used for carrying out initial feature point matching; thirdly, a parameter space of Hough conversion is set according to basic information of the feature point; fourthly, the Hough parameter space is voted by having access to each pair of matching points; fifthly, the matching point pair in the Hough parameter subspace with the largest vote is obtained, and further processing is carried out through a random sample consensus(RANSAC) algorithm. According to the error matching point pair removing algorithm in image registration, the Hough conversion method is skillfully used for carrying out primary purification, and the proportion of the error matching point pair can be rapidly and efficiently reduced through primary purification.
Description
Technical field
The present invention relates to pattern-recognition and digital image processing field, particularly the Mismatching point in a kind of image registration is to rejecting algorithm.
Background technology
In the demand that image is carried out to registration practical application in a lot of fields, be all suggested, as the multi-layer image registration of CT in rebuilding, remotely-sensed data analysis, Model Reconstruction etc.Through searching in the matching double points that arest neighbors obtains, may have Mismatching point pair, these, need to be these points to rejecting to normally not meeting the transformation relation of image.Current main Mismatching point has least square intermediate value (Least Median of Squares, LMedS) algorithm and RANSAC algorithm to rejecting algorithm.In the image registration of rebuilding at CT, there is more similar area, can produce a large amount of Mismatching points pair, use existing LMedS algorithm and RANSAC algorithm often cannot meet the requirement of time performance and accuracy rate simultaneously.This just requires to propose new method when Mismatching point comparative example is higher, still can the short time, high-accuracy ground rejects Mismatching point pair.
When Mismatching point is lower to proportion, LMedS can reject the impact of exterior point theoretically completely, obtain optimum projective transformation matrix and estimate, and computing time and Mismatching point little to proportion relation.But after exterior point surpasses 50% in sample set ratio, due to the each record of LMedS be intermediate value deviation, LMedS algorithm is accurate computational transformation parameter just.Matching double points sum is respectively 500,1000,1500,2000 o'clock, and along with the raising of the shared comparative example of Mismatching point, the time performance of LMedS algorithm and correct identification rate of change are respectively as shown in Fig. 2 (a) and Fig. 2 (b).
RANSAC algorithm still can effectively be processed when Mismatching point comparative example surpasses 50%, but Mismatching point will directly affect the number of times of RANSAC algorithm iteration to shared ratio.When Mismatching point comparative example is larger, the number of times of RANSAC algorithm iteration is also just more, causes consuming time more serious.Matching double points sum is respectively 500,1000,1500,2000 o'clock, and along with the raising of the shared comparative example of Mismatching point, the time performance of RANSAC algorithm and correct identification rate of change are as shown in Fig. 3 (a) and Fig. 3 (b).
Summary of the invention
The present invention is in order to overcome the deficiency of existing algorithm when Mismatching point comparative example is higher, provides a kind of and can guarantee that the time is short, precision is high Mismatching point is to elimination method simultaneously.
In order to achieve the above object, the present invention is by the following technical solutions:
Mismatching point in image registration, to rejecting an algorithm, comprises the steps:
S1: use the feature detection with yardstick unchangeability, rotational invariance find out the unique point of image and obtain descriptor with description operator;
S2: use k-d tree algorithm to carry out Feature Points Matching, obtain initial matching point pair set;
S3: utilize the essential information of unique point that the parameter space that Hough converts is set;
S4: access every pair of matching double points, Hough parameter space is voted;
S5: obtain the matching double points in who gets the most votes Hough Parameter Subspace, do further processing with RANSAC algorithm and reject Mismatching point pair.
Preferably, described step S3 specifically comprises the steps:
S3.1: select parameter space, supposing has a unique point a in image 1
i, its position, yardstick, rotation parameter are respectively: (x
i, y
i), s
i, θ
i, this some match point a ' in image 2
iparameters be respectively: (x '
i, y '
i), s '
i, θ '
i; Translation between two unique points is so: (dx, dy)=(x '
i-x
i, y '
i-y
i); Dimensional variation is: ds=s '
i/ s
i; Rotation angle is changed to: d θ=θ '
i-θ
i; Utilize four parameter spaces that parameter d x, dy, ds, d θ convert as Hough above;
S3.2: set the width between each parameter region; Parameters subspace size is that rotation dimension interval width is 30 °, and scale dimension is 2, and location dimension interval width is relevant with image size, and the interval width of dx, dy dimension is respectively that image subject to registration is grown and is wide by 1/4th.
Preferably, described step S4 specifically comprises the steps:
S4.1: use one dimension Hash table as the data structure of Hough parameter space;
S4.2: access one by one matching double points, calculate (dx, dy, ds, d θ);
S4.3: in order to reduce the impact of boundary effect, all throw between two nearest parameter regions during matching double points ballot in every one dimension parameter, a pair of like this match point can be voted to 16 Parameter Subspaces, calculates this 16 sub spaces;
S4.4: 16 Parameter Subspaces that draw in step S4.3, after calculating, hash function in S4.1 finds corresponding variable position respectively, and counting adds 1, and stores matching double points information.
Preferably, described step S5 specifically comprises the steps:
S5.1: traversal Hough parameter space, find out who gets the most votes subspace;
S5.2: use in S5.1 and obtain the matching double points set in subspace, calculate the projective transformation matrix H between registering images by RANSAC algorithm;
S5.3: traversal initial matching point pair set, to every couple of matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points.
Preferably, in step S5.2, the concrete steps that calculate the projective transformation matrix H between registering images by RANSAC algorithm are:
S5.2.1: by the correct matching double points number N of current optimum estimate
ibe set to 0;
S5.2.2: choose at random 4 pairs of initial matching points the matching double points set of obtaining from S5.1 right, need to guarantee the point chosen to any 3 points in each image not point-blank, if there are conllinear, again selected points pair at 3; Between two images, current projective transformation matrix H1 can be according to 4 groups of points to calculating;
S5.2.3: remaining matching double points in the matching double points set that traversal S5.1 obtains, matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points;
S5.2.4: more current correct matching double points number, if be greater than N
icurrent Transform matrix H 1 is that current best matrix is estimated, upgrades N simultaneously
ivalue;
S5.2.5: selecting the transformation matrix H1 that correct matching double points number is maximum after several times random sampling is calculated is the projective transformation matrix H between image.
Compared with prior art, tool has the following advantages and beneficial effect in the present invention:
1, the present invention utilizes Hough transform method to carry out preliminary purification dexterously, and preliminary purification can reduce Mismatching point quickly and efficiently to proportion.Experiment shows, when Mismatching point comparative example accounts for 80%, after preliminary purification, Mismatching point comparative example can be down to 50% left and right.
2, the present invention, after preliminary purification, uses RANSAC algorithm further to process assurance precision.When Mismatching point comparative example is higher, the present invention can guarantee that working time is short, precision is high simultaneously.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the inventive method;
Fig. 2 (a) is to the LMedS algorithm time performance analysis figure under number and Mismatching point comparative example at Different matching point;
Fig. 2 (b) is to the LMedS algorithm identified ratio chart under number and Mismatching point comparative example at Different matching point;
Fig. 3 (a) is to the RANSAC algorithm time performance analysis figure under number and Mismatching point comparative example at Different matching point;
Fig. 3 (b) is to the RANSAC algorithm identified Ratio Analysis Diagram under number and Mismatching point comparative example at Different matching point;
Fig. 4 (a) for the present invention at Different matching point to the time performance analysis figure under number and Mismatching point comparative example;
Fig. 4 (b) for the present invention at Different matching point to the identification ratio analysis under number and Mismatching point comparative example.
Embodiment
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.
As shown in Figure 1, a kind of Mismatching point in image registration of the present invention, to elimination method, comprises the following steps:
Object of the present invention is achieved through the following technical solutions:
S1: use the feature detection with yardstick unchangeability, rotational invariance find out the unique point of image and obtain descriptor with description operator.
S2: use k-d tree algorithm to carry out Feature Points Matching, obtain initial matching point pair set.
S3: utilize the essential information of unique point that the parameter space that Hough converts is set.
S3.1: select parameter space.Supposing has a unique point a in image 1
i, its position, yardstick, rotation parameter are respectively: (x
i, y
i), s
i, θ
i, this some match point a ' in image 2
iparameters be respectively: (x '
i, y '
i), s '
i, θ '
i.Translation between two unique points is so: (dx, dy)=(x '
i-x
i, y '
i-y
i); Dimensional variation is: ds=s '
i/ s
i; Rotation angle is changed to: d θ=θ '
i-θ
i.Utilize above four parameter d x, dy, ds, d θ as the parameter space of Hough transformation.
S3.2: set the width between each parameter region.When the discrete segment of dx, dy, ds, d θ is set, if interval is too large, can introduce larger error; If interval is too little, can cause the consumption of algorithm room and time too large, simultaneously can be too responsive to factors such as noises, there is larger boundary error.Herein parameters subspace size is that rotation dimension interval width is 30 °, and scale dimension is 2, and location dimension interval width is relevant with image size, and the interval width of dx, dy dimension is respectively image length subject to registration and wide by 1/4th.
S4: access every pair of matching double points, Hough parameter space is voted.
S4.1: use one dimension Hash table as the data structure of Hough parameter space.
S4.2: access one by one matching double points, calculate (dx, dy, ds, d θ).
S4.3: in order to reduce the impact of boundary effect, all throw between two nearest parameter regions during matching double points ballot in every one dimension parameter, a pair of like this match point can be voted to 16 Parameter Subspaces.Calculate this 16 sub spaces.
S4.4: 16 Parameter Subspaces that draw in step S4.3, after calculating, hash function in S4.1 finds corresponding variable position respectively, and counting adds 1, and stores matching double points information.
S5: obtain the matching double points in who gets the most votes Hough Parameter Subspace, do further processing with RANSAC algorithm and reject Mismatching point pair.
S5.1: traversal Hough parameter space, find out who gets the most votes subspace.
S5.2: use in S5.1 and obtain the matching double points set in subspace, calculate the projective transformation matrix H between registering images by RANSAC algorithm.
The concrete steps that calculate the projective transformation matrix H between registering images by RANSAC algorithm are:
S5.2.1: by the correct matching double points number N of current optimum estimate
ibe set to 0;
S5.2.2: choose at random 4 pairs of initial matching points the matching double points set of obtaining from S5.1 right, need to guarantee the point chosen to any 3 points in each image not point-blank, if there are conllinear, again selected points pair at 3; Between two images, current projective transformation matrix H1 can be according to 4 groups of points to calculating;
S5.2.3: remaining matching double points in the matching double points set that traversal S5.1 obtains, matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points;
S5.2.4: more current correct matching double points number, if be greater than N
icurrent Transform matrix H 1 is that current best matrix is estimated, upgrades N simultaneously
ivalue;
S5.2.5: selecting the transformation matrix H1 that correct matching double points number is maximum after several times random sampling is calculated is the projective transformation matrix H between image.
S5.3: traversal initial matching point pair set, to every couple of matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points.
Matching double points sum is respectively 500,1000,1500,2000 o'clock, and along with the raising of the shared comparative example of Mismatching point, time performance of the present invention and correct identification rate of change are as shown in Fig. 4 (a) and Fig. 4 (b).
Above-described embodiment is preferably embodiment of the present invention; but embodiments of the present invention are not restricted to the described embodiments; other any do not deviate from change, the modification done under Spirit Essence of the present invention and principle, substitutes, combination, simplify; all should be equivalent substitute mode, within being included in protection scope of the present invention.
Claims (5)
1. the Mismatching point in image registration, to rejecting an algorithm, is characterized in that, comprises the steps:
S1: use the feature detection with yardstick unchangeability, rotational invariance find out the unique point of image and obtain descriptor with description operator;
S2: use k-d tree algorithm to carry out Feature Points Matching, obtain initial matching point pair set;
S3: utilize the essential information of unique point that the parameter space that Hough converts is set;
S4: access every pair of matching double points, Hough parameter space is voted;
S5: obtain the matching double points in who gets the most votes Hough Parameter Subspace, do further processing with RANSAC algorithm and reject Mismatching point pair.
2. the Mismatching point in image registration according to claim 1, to rejecting algorithm, is characterized in that, described step S3 specifically comprises the steps:
S3.1: select parameter space, supposing has a unique point a in image 1
i, its position, yardstick, rotation parameter are respectively: (x
i, y
i), s
i, θ
i, this some match point a ' in image 2
iparameters be respectively: (x '
i, y '
i), s '
i, θ '
i; Translation between two unique points is so: (dx, dy)=(x '
i-x
i, y '
i-y
i); Dimensional variation is: ds=s '
i/ s
i; Rotation angle is changed to: d θ=θ '
i-θ
i; Utilize four parameter spaces that parameter d x, dy, ds, d θ convert as Hough above;
S3.2: set the width between each parameter region; Parameters subspace size is that rotation dimension interval width is 30 °, and scale dimension is 2, and location dimension interval width is relevant with image size, and the interval width of dx, dy dimension is respectively that image subject to registration is grown and is wide by 1/4th.
3. the Mismatching point in image registration according to claim 1, to rejecting algorithm, is characterized in that, described step S4 specifically comprises the steps:
S4.1: use one dimension Hash table as the data structure of Hough parameter space;
S4.2: access one by one matching double points, calculate (dx, dy, ds, d θ);
S4.3: in order to reduce the impact of boundary effect, all throw between two nearest parameter regions during matching double points ballot in every one dimension parameter, a pair of like this match point can be voted to 16 Parameter Subspaces, calculates this 16 sub spaces;
S4.4: 16 Parameter Subspaces that draw in step S4.3, after calculating, hash function in S4.1 finds corresponding variable position respectively, and counting adds 1, and stores matching double points information.
4. the Mismatching point in image registration according to claim 1, to rejecting algorithm, is characterized in that, described step S5 specifically comprises the steps:
S5.1: traversal Hough parameter space, find out who gets the most votes subspace;
S5.2: use in S5.1 and obtain the matching double points set in subspace, calculate the projective transformation matrix H between registering images by RANSAC algorithm;
S5.3: traversal initial matching point pair set, to every couple of matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points.
5. the Mismatching point in image registration according to claim 4, to rejecting algorithm, is characterized in that, in step S5.2, the concrete steps that calculate the projective transformation matrix H between registering images by RANSAC algorithm are:
S5.2.1: by the correct matching double points number N of current optimum estimate
ibe set to 0;
S5.2.2: choose at random 4 pairs of initial matching points the matching double points set of obtaining from S5.1 right, need to guarantee the point chosen to any 3 points in each image not point-blank, if there are conllinear, again selected points pair at 3; Between two images, current projective transformation matrix H1 can be according to 4 groups of points to calculating;
S5.2.3: remaining matching double points in the matching double points set that traversal S5.1 obtains, matching double points (a
i, a '
i) ask a
iby H convert obtain position and registration point position poor dv=d (a '
i, Ha
i), if dv is greater than setting threshold, puts for Mismatching point pair, otherwise be correct matching double points;
S5.2.4: more current correct matching double points number, if be greater than N
icurrent Transform matrix H 1 is that current best matrix is estimated, upgrades N simultaneously
ivalue;
S5.2.5: selecting the transformation matrix H1 that correct matching double points number is maximum after several times random sampling is calculated is the projective transformation matrix H between image.
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| CN106886977A (en) * | 2017-02-08 | 2017-06-23 | 徐州工程学院 | A kind of many figure autoregistrations and anastomosing and splicing method |
| CN107220580A (en) * | 2016-03-22 | 2017-09-29 | 敦泰电子有限公司 | Image recognition grab sample unification algorism based on ballot decision-making and least square method |
| CN108510436A (en) * | 2018-03-28 | 2018-09-07 | 清华大学 | Reconstruction parameter searching method and system in a kind of Ice mapping three-dimensionalreconstruction |
| CN110659654A (en) * | 2019-09-24 | 2020-01-07 | 福州大学 | Drawing duplicate checking and plagiarism preventing method based on computer vision |
| CN111009001A (en) * | 2019-09-17 | 2020-04-14 | 哈工大机器人(中山)无人装备与人工智能研究院 | Image registration method, device, equipment and storage medium |
| CN112070813A (en) * | 2020-08-21 | 2020-12-11 | 国网山东省电力公司青岛供电公司 | Feature matching method based on connection feature consistency |
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| CN104778675A (en) * | 2015-04-28 | 2015-07-15 | 中国矿业大学 | Coal mining fully mechanized excavation face dynamic video image fusion method |
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| CN107220580A (en) * | 2016-03-22 | 2017-09-29 | 敦泰电子有限公司 | Image recognition grab sample unification algorism based on ballot decision-making and least square method |
| CN107220580B (en) * | 2016-03-22 | 2022-08-09 | 敦泰电子有限公司 | Image identification random sampling consistent algorithm based on voting decision and least square method |
| CN106886977A (en) * | 2017-02-08 | 2017-06-23 | 徐州工程学院 | A kind of many figure autoregistrations and anastomosing and splicing method |
| CN108510436A (en) * | 2018-03-28 | 2018-09-07 | 清华大学 | Reconstruction parameter searching method and system in a kind of Ice mapping three-dimensionalreconstruction |
| CN108510436B (en) * | 2018-03-28 | 2020-09-15 | 清华大学 | Method and system for searching reconstruction parameters in three-dimensional reconstruction of cryoelectron microscope |
| CN111009001A (en) * | 2019-09-17 | 2020-04-14 | 哈工大机器人(中山)无人装备与人工智能研究院 | Image registration method, device, equipment and storage medium |
| CN110659654A (en) * | 2019-09-24 | 2020-01-07 | 福州大学 | Drawing duplicate checking and plagiarism preventing method based on computer vision |
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Application publication date: 20141001 |