CN113222943B - Image deformation estimation method based on mixed grid transformation model - Google Patents

Image deformation estimation method based on mixed grid transformation model Download PDF

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CN113222943B
CN113222943B CN202110539834.XA CN202110539834A CN113222943B CN 113222943 B CN113222943 B CN 113222943B CN 202110539834 A CN202110539834 A CN 202110539834A CN 113222943 B CN113222943 B CN 113222943B
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characteristic point
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CN113222943A (en
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杨宪强
张智浩
孙昊
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Ningbo Intelligent Equipment Research Institute Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • 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
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image

Abstract

An image deformation estimation method based on a hybrid grid transformation model belongs to the field of computer vision and image processing. The method solves the problem that the inter-image transformation model obtained by the existing estimation method cannot effectively solve the discontinuous deformation. Firstly, extracting and matching feature points of an image; then, carrying out primary classification and screening on the characteristic point pairs; and finally, estimating a hybrid grid transformation model. The method can accurately estimate the mixed grid transformation model and the component number of the mixed grid transformation model, and the estimated mixed grid transformation model can effectively solve the problem of discontinuous deformation. The invention can be used for estimating the image deformation.

Description

Image deformation estimation method based on mixed grid transformation model
Technical Field
The invention belongs to the field of computer vision and image processing, and particularly relates to an image deformation estimation method based on a hybrid grid transformation model.
Background
Image registration is a common problem in the field of computer vision and image processing, and can be applied to occasions such as image splicing, target positioning, three-dimensional reconstruction and the like. When an image of a real scene is shot, due to the fact that the distances between the positions of different objects and a camera are different, object displacement discontinuity exists in the images shot from different viewpoints, image registration operation needs to be conducted on the images shot from the different viewpoints, but the problem of discontinuous deformation cannot be effectively solved through an inter-image transformation model obtained through an existing estimation method.
Disclosure of Invention
The invention aims to solve the problem that a transformation model between images obtained by the existing estimation method cannot effectively solve the discontinuous deformation, and provides an image deformation estimation method based on a mixed grid transformation model.
The technical scheme adopted by the invention for solving the technical problems is as follows: an image deformation estimation method based on a hybrid mesh transformation model specifically comprises the following steps:
step one, respectively extracting reference images I of the same scene shot under different visual angles0And a target image I1And for reference image I0SIFT feature point and target image I1The SIFT feature points are matched to obtain an initial feature point pair set S0，
Figure BDA0003071219330000011
Wherein the content of the first and second substances,
Figure BDA0003071219330000012
as the pair of initial characteristic points,
Figure BDA0003071219330000013
for reference picture I0The coordinates of the feature point in (2),
Figure BDA0003071219330000014
is a target image I1Characteristic point coordinates of (1), N0Is a set S0The number of the middle initial characteristic point pairs;
step two, classifying and screening the initial characteristic point pairs obtained in the step one, wherein the set of the characteristic point pairs obtained through screening is S,
Figure BDA0003071219330000015
(xi,yi) For pairs of characteristic points, x, obtained by screeningiFor reference picture I0Coordinates of the feature points in (1), yiIs a target image I1The coordinate of the characteristic point in the set S, N is the number of the characteristic point pairs in the set S, and the characteristic point coordinates in the set S are obtainedThe total number of categories of characteristic point pairs included in the set S is represented as K, and the category of the ith characteristic point pair is represented as c for each category of characteristic point pairi，ci＝1,...,K；
Step three, for the reference image I0Carrying out grid division, wherein the width of each grid is w, and the height of each grid is h; taking a mixed grid transformation model consisting of K grid transformation models as a reference image I0And a target image I1Inter-geometric transformation models, each mesh transformation model constituting a component of the hybrid mesh transformation model;
wherein, the grid deformation parameter to be solved of each component is wk，k＝1,2,…,K，wkIs the initial covariance matrix of
Figure BDA0003071219330000021
Figure BDA0003071219330000022
Is a matrix of0The inverse matrix of (d);
step four, respectively calculating each characteristic point xiCorresponding intermediate variable matrix phii；
Initializing a responsibility matrix R, wherein the size of R is NxK, and the ith row and kth column elements in the responsibility matrix R are Ri,kIf k is equal to ciThen r isi,k1, otherwise ri,k0; initializing the iteration number n to be 0;
step six, for each component, utilizing phiiAnd R calculates wkMean value m of the posterior probability distribution ofkCovariance matrix
Figure BDA0003071219330000023
And a parameter alphak；
Figure BDA0003071219330000024
Figure BDA0003071219330000025
Figure BDA0003071219330000026
Wherein the covariance matrix
Figure BDA0003071219330000027
Is ΛkInverse matrix of, beta and alpha0Is a constant;
step seven, utilizing mk、
Figure BDA0003071219330000028
And alphakUpdating element values R in the responsibility matrix Ri,kThe updated element value is r'i,k；
And step eight, updating the iteration number n to n +1 according to the element value r 'updated in the step seven'i,kComputing
Figure BDA0003071219330000029
Step nine, repeating the process from the step six to the step eight until n is more than 1 and
Figure BDA00030712193300000210
stopping iteration and obtaining m in the last iterationkAs wkAn estimate of (d).
The invention has the beneficial effects that: the invention provides an image deformation estimation method based on a hybrid grid transformation model, which comprises the steps of firstly extracting and matching characteristic points of an image; then, carrying out primary classification and screening on the characteristic point pairs; and finally, estimating a hybrid grid transformation model. The method can accurately estimate the mixed grid transformation model and the component number of the mixed grid transformation model, and the estimated mixed grid transformation model can effectively solve the problem of discontinuous deformation.
Drawings
FIG. 1 is a flow chart of an image deformation estimation method based on a hybrid mesh transformation model according to the present invention;
FIG. 2a) is the same scene graph one taken at a different viewpoint;
FIG. 2b) is the same scene graph two taken at a different viewpoint;
the characteristic points and grid lines of each component of the hybrid grid transformation model are represented in the figure.
Detailed Description
First embodiment this embodiment will be described with reference to fig. 1. The method for estimating image deformation based on a hybrid mesh transformation model according to the present embodiment specifically includes the following steps:
step one, respectively extracting reference images I of the same scene shot under different visual angles0And a target image I1And for reference image I0SIFT feature point and target image I1The SIFT feature points are matched to obtain an initial feature point pair set S0，
Figure BDA0003071219330000031
Wherein the content of the first and second substances,
Figure BDA0003071219330000032
is the initial characteristic point pair, and the characteristic point pair,
Figure BDA0003071219330000033
for reference picture I0The coordinates of the feature points in (1),
Figure BDA0003071219330000036
is a target image I1Characteristic point coordinates of (1), N0Is a set S0The number of the middle initial characteristic point pairs;
SIFT is a commonly used feature point extraction and description algorithm, has the invariance of illumination, rotation and deformation to a certain degree, and is a commonly used method because the extracted feature points are stable and have high quality;
step two, classifying and screening the initial characteristic point pairs obtained in the step one, wherein the set of the characteristic point pairs obtained through screening isS，
Figure BDA0003071219330000034
(xi,yi) For pairs of characteristic points, x, obtained by screeningiFor reference picture I0Coordinates of the feature points in (1), yiIs a target image I1N is the number of characteristic point pairs in the set S, and the category of each characteristic point pair in the set S is obtained, the total number of categories of characteristic point pairs included in the set S is represented as K, and the category of the ith characteristic point pair is represented as ci，ci＝1,...,K；
Step three, as shown in fig. 2a) and 2 b); for reference image I0Carrying out grid division, wherein the width of each grid is w, and the height of each grid is h; taking a mixed grid transformation model consisting of K grid transformation models as a reference image I0And a target image I1Inter-geometric transformation models, each mesh transformation model constituting a component of the hybrid mesh transformation model;
wherein, the grid deformation parameter to be solved of each component is wk，k＝1,2,…,K，wkIs the initial covariance matrix of
Figure BDA0003071219330000035
Is a matrix of0The inverse matrix of (d);
step four, respectively calculating each characteristic point xiCorresponding intermediate variable matrix phiiPhi matrixiThe size of (a) is 2M × 2;
initializing a responsibility matrix R, wherein the size of R is NxK, and the ith row and kth column elements in the responsibility matrix R are Ri,kIf k is equal to ciThen r isi,k1, otherwise ri,k0; initializing the iteration number n to be 0;
step six, for each component, utilizing phiiAnd R calculates wkMean value m of the posterior probability distribution ofkCovariance matrix
Figure BDA0003071219330000041
And a parameter alphak；
Figure BDA0003071219330000042
Figure BDA0003071219330000043
Figure BDA0003071219330000044
Wherein the covariance matrix
Figure BDA0003071219330000045
Is ΛkInverse matrix of, beta and alpha0Is a constant;
step seven, utilizing mk、
Figure BDA0003071219330000046
And alphakUpdating element values R in the responsibility matrix Ri,kThe updated element value is r'i,k；
And step eight, updating the iteration number n to n +1 according to the element value r 'updated in the step seven'i,kComputing
Figure BDA0003071219330000047
Step nine, repeating the process from the step six to the step eight until n is more than 1 and
Figure BDA0003071219330000048
stopping iteration and obtaining m in the last iterationkAs wkAn estimate of (d).
I.e. when n > 1 and all components are satisfied simultaneously
Figure BDA0003071219330000049
Then m obtained in the last iteration is addedkAs wkIs estimated value of. In the iteration process, the element value R in the updated responsibility matrix R obtained in the iteration is usedi,kFor the next iteration process.
Estimating the model by the expectation maximization iteration step has the advantages of avoiding overfitting and automatically determining the number of model components.
The second embodiment is as follows: the difference between this embodiment and the first embodiment is that, in the first step, the reference image I is searched based on the FLANN fast nearest neighbor search library0SIFT feature point and target image I1The SIFT feature points are matched.
The FLANN fast nearest neighbor search library can realize fast nearest neighbor matching of high-dimensional feature points.
The third concrete implementation mode: the difference between this embodiment and the second embodiment is that the method for classifying and screening the initial feature point pairs obtained in the first step is a RANSAC algorithm.
RANSAC is a parameterized model estimation algorithm and is characterized in that the method is robust to abnormal points and can obtain a correct model of effective sample data.
The fourth concrete implementation mode is as follows: the third difference between this embodiment and the specific embodiment is that the classification and screening of the initial feature point pairs obtained in step one includes the following specific processes:
step two, setting the current residual characteristic point pair set as S', and initializing the set as S0(ii) a Representing the characteristic point pair set obtained through screening as S, and initializing the set S into an empty set; initializing the serial number of the current category as c' ═ 1;
secondly, extracting interior points from the set S' by adopting RANSAC algorithm to obtain an interior point set Sc′Setting an inner point distance threshold value as Q and iteration times as L;
step two and three, if Sc′If the number of the characteristic point pairs is more than or equal to 10, updating S to S + Sc′And will aggregate Sc′Setting the class number of the middle characteristic point pair to be c ' and updating S ' ═ S ' -Sc′，c′＝c′+1；
The second step four,Repeatedly executing the second step and the third step until S appearsc′If the number of the characteristic point pairs is less than 10, the process is ended.
The fifth concrete implementation mode: the fourth difference between this embodiment and the fourth embodiment is that the model selected by the RANSAC algorithm is a homography matrix transformation model.
The sixth specific implementation mode: the fourth difference between this embodiment and the specific embodiment is that the value of the interior point distance threshold Q is 3, and the value of the iteration number L is 500.
The seventh embodiment: the sixth embodiment is different from the sixth embodiment in that the matrix Λ0The expression of (a) is:
Λ0＝cUTU
wherein c is a constant, and the value is 5 in the invention; u is a sparse matrix of 2 Mx 2M size, M is for reference image I0The number of grid points obtained by grid division and the element U of the mth row and mth column in the sparse matrix Um,m1, mth row of U
Figure BDA0003071219330000051
Elements of a column
Figure BDA0003071219330000052
Figure BDA0003071219330000053
The number of the grid points connected to the mth grid point is the serial number of the grid point connected to the mth grid point (according to the actual situation, the number of the grid points connected to the mth grid point may be 2,3, or 4), and the remaining elements in U are all 0; the superscript T stands for transpose.
The specific implementation mode is eight: the difference between this embodiment and the seventh embodiment is that the specific process of step four is as follows:
characteristic point xiThe sequence numbers of 4 grid points of the grid are
Figure BDA0003071219330000054
The bilinear interpolation weights of 1,2,3,4 and 4 grid points are respectively
Figure BDA0003071219330000055
Then the intermediate variable matrix phiiTo (1) a
Figure BDA0003071219330000056
The row column 1 element has a value of
Figure BDA0003071219330000057
First, the
Figure BDA0003071219330000058
Row column 2 element has a value of
Figure BDA0003071219330000059
The values of the remaining elements are all 0.
In the present invention, the numbers of the grid points may be arranged in any manner, but it is only required that the grid point numbers in this embodiment and the grid point numbers in the seventh embodiment are obtained in the same arrangement manner.
The specific implementation method nine: the present embodiment is different from the first embodiment in that the value of the constant β is 0.05.
The detailed implementation mode is ten: in this embodiment, the constant α is different from the first embodiment0Is 0.01.
The concrete implementation mode eleven: the difference between this embodiment and the eighth embodiment is that the specific process of the seventh step is as follows:
Figure BDA0003071219330000061
where ρ isi,kIs an intermediate variable;
Figure BDA0003071219330000062
where ψ (-) is a double gamma function and tr (-) is the trace of the matrix.
The above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.

Claims (9)

1. An image deformation estimation method based on a hybrid mesh transformation model is characterized by specifically comprising the following steps:
step one, respectively extracting reference images I of the same scene shot under different visual angles0And a target image I1And for reference image I0SIFT feature point and target image I1The SIFT feature points are matched to obtain an initial feature point pair set S0，
Figure FDA0003517128270000011
Wherein the content of the first and second substances,
Figure FDA0003517128270000012
as the pair of initial characteristic points,
Figure FDA0003517128270000013
for reference picture I0The coordinates of the feature points in (1),
Figure FDA0003517128270000014
is a target image I1Characteristic point coordinates of (1), N0Is a set S0The number of the middle initial characteristic point pairs;
step two, classifying and screening the initial characteristic point pairs obtained in the step one, wherein the set of the characteristic point pairs obtained through screening is S,
Figure FDA0003517128270000015
(xi,yi) For pairs of characteristic points, x, obtained by screeningiFor reference picture I0Coordinates of the feature points in (1), yiIs a target image I1N is the number of characteristic point pairs in the set S, and the category of each characteristic point pair in the set S is obtained, the total number of categories of characteristic point pairs included in the set S is represented as K, and the category of the ith characteristic point pair is represented as ci，ci＝1,...,K；
Step three, for the reference image I0Carrying out grid division, wherein the width of each grid is w, and the height of each grid is h; taking a mixed grid transformation model consisting of K grid transformation models as a reference image I0And a target image I1Inter-geometric transformation models, each mesh transformation model constituting a component of the hybrid mesh transformation model;
wherein, the grid deformation parameter to be solved of each component is wk，k＝1,2,…,K，wkIs the initial covariance matrix of
Figure FDA0003517128270000016
Figure FDA00035171282700000113
Is a matrix of0The inverse matrix of (d);
step four, respectively calculating each characteristic point xiCorresponding intermediate variable matrix phii；
The specific process of the step four is as follows:
characteristic point xiThe sequence numbers of the 4 grid points of the grid are
Figure FDA0003517128270000017
The bilinear interpolation weights of the 4 grid points are respectively
Figure FDA0003517128270000018
Then the intermediate variable matrix phiiTo (1) a
Figure FDA0003517128270000019
The row column 1 element has a value of
Figure FDA00035171282700000110
First, the
Figure FDA00035171282700000111
Row column 2 element has a value of
Figure FDA00035171282700000112
The values of the other elements are all 0;
initializing a responsibility matrix R, wherein the size of R is NxK, and the ith row and kth column elements in the responsibility matrix R are Ri,kIf k is equal to ciThen r isi,k1, otherwise ri,k0; initializing the iteration number n to be 0;
step six, for each component, utilizing phiiAnd R calculates wkMean value m of the posterior probability distribution ofkCovariance matrix
Figure FDA0003517128270000021
And a parameter alphak；
Figure FDA0003517128270000022
Figure FDA0003517128270000023
Figure FDA0003517128270000024
Wherein the covariance matrix
Figure FDA0003517128270000025
Is ΛkInverse matrix of, beta and alpha0Is a constant;
step seven, utilizing mk、
Figure FDA0003517128270000026
And alphakUpdating element values R in the responsibility matrix Ri,kWhere the updated element value is denoted by r'i,k；
The concrete process of the seventh step is as follows:
Figure FDA0003517128270000027
where ρ isi,kIs an intermediate variable;
Figure FDA0003517128270000028
wherein psi (-) is a double gamma function, and tr (-) is the trace of the matrix;
and step eight, updating the iteration number n to n +1 according to the element value r 'updated in the step seven'i,kComputing
Figure FDA0003517128270000029
Step nine, repeating the process from the step six to the step eight until n is more than 1 and
Figure FDA00035171282700000210
stopping iteration and obtaining m in the last iterationkAs wkAn estimate of (d).
2. The method according to claim 1, wherein in the first step, the reference image I is estimated based on a FLANN fast nearest neighbor search library0SIFT feature point and target image I1The SIFT feature points are matched.
3. The method for estimating image deformation based on hybrid mesh transform model as claimed in claim 2, wherein the initial feature point pairs obtained in the first step are classified and screened by using RANSAC algorithm.
4. The method according to claim 3, wherein the classifying and screening of the initial feature point pairs obtained in the first step comprises:
step two, setting the current residual feature point pair set as S', and initializing the feature point pair set as S0(ii) a Representing the characteristic point pair set obtained through screening as S, and initializing the set S into an empty set; initializing the serial number of the current category as c' ═ 1;
secondly, extracting interior points from the set S' by adopting RANSAC algorithm to obtain an interior point set Sc′Setting an inner point distance threshold value as Q and iteration times as L;
step two and step three, if Sc′If the number of the characteristic point pairs is more than or equal to 10, updating S to S + Sc′And will aggregate Sc′Setting the class number of the middle characteristic point pair to be c ' and updating S ' ═ S ' -Sc′，c′＝c′+1；
Step two and step four, repeatedly executing step two and step three until S appearsc′If the number of the characteristic point pairs is less than 10, the process is ended.
5. The method for image deformation estimation based on hybrid grid transformation model as claimed in claim 4, wherein the RANSAC algorithm uses a homography matrix transformation model to classify and screen the initial feature point pairs.
6. The image deformation estimation method based on the hybrid mesh transformation model according to claim 4, wherein the value of the interior point distance threshold Q is 3, and the value of the iteration number L is 500.
7. The image deformation estimation method based on the hybrid mesh transformation model as claimed in claim 6, wherein the matrix Λ is0The expression of (a) is:
Λ0＝cUTU
wherein c is a constant; u is a sparse matrix of 2 Mx 2M size, M is for reference image I0The number of grid points obtained by grid division and the element U of the mth row and mth column in the sparse matrix Um,m1, mth row of U
Figure FDA0003517128270000031
Elements of a column
Figure FDA0003517128270000032
Figure FDA0003517128270000033
The number of the grid point connected with the mth grid point is the serial number of the grid point, and the rest elements in the U are 0; the superscript T stands for transpose.
8. The image deformation estimation method based on the hybrid mesh transform model according to claim 1, wherein the value of the constant β is 0.05.
9. The method of claim 1, wherein the constant α is the same as the constant α0Is 0.01.
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