CN105488754A - Local linear migration and affine transformation based image feature matching method and system - Google Patents

Abstract

The invention proposes a local linear migration and affine transformation based image feature matching method and system used for establishing correct matching by removing error matching in an initial matching point pair. The method comprises: establishing a model corresponding to geometric transformation of to-be-matched images for affine transformation of the to-be-matched images, establishing a model corresponding to a posterior probability of correct matching of the matching point pair, and solving model parameters based on nearest neighbor matching points, a least square method and an optimization method; and calculating the posterior probability of the correct matching of the initial matching point pair, and judging the correctness of the initial matching point pair according to a threshold. According to the method, the modeling is carried out for the affine transformation of the to-be-matched images, so that the error rate of matching is greatly reduced and good robustness is still kept even if large amounts of error matching exists in initial matching.

Description

Based on local linear migration and the Image Feature Matching method and system of affined transformation

Technical field

The present invention relates to Image Feature Matching technical field, specifically, the present invention relates to the Image Feature Matching technical scheme based on local linear migration and affined transformation.

Background technology

The elementary object of images match is that the same area of two width images of the same scene using different sensors to obtain under different time and visual angle is carried out correspondence.

In the past few decades, scholars have studied a lot of method and solve remote sensing images matching problem.These methods roughly can be divided into two classes: based on matching process and the feature-based matching method in region.The former finds match information by the similarity degree searching for original gray value in certain area in two width images; Latter utilizes the descriptor similarity of local feature or space geometry constraint to find matching double points.When having a small amount of significantly details, the information that gray-scale value provides than local shape and structure many, thus better based on the matching effect of the method in region.But large based on the method calculated amount in region, and inapplicable when image fault and luminosity change.On the contrary, characterization method has better robustness, can process the image of babble, be widely used.

How to find match point corresponding in two width images, form matching double points, and guarantee that the correctness of matching double points is the key of image matching method.

Matching process based on region mainly contains correlation method, fourier methods and mutual information method three kinds.The main thought of correlation method is the similarity of corresponding window in calculating two width image, then will have a pair of maximum similarity degree as matching double points.But correlation method cannot be suitable for without texture region similarity is inapparent, and calculation of complex.Fourier methods make use of the Fourier Representations of image in frequency field.Compared with traditional correlation technique, this method is higher and have good robustness to frequency noise like in counting yield.But the image that this method has different spectrum structure in process has necessarily restricted.Although its matching effect of mutual information method is good, it cannot obtain the global maximum in whole search volume, therefore inevitably lowers its robustness.

In feature-based matching method, usually adopt a kind of strategy being divided into two steps.The first step, determine one group of initial matching double points by the similarity degree of Feature Descriptor, wherein the overwhelming majority is correct coupling, but inevitably containing a large amount of erroneous matching.Second step, by being removed the coupling of mistake by geometrical constraint, finally just can obtain the geometric parameter converted between correct matching double points and two width images.The exemplary of this strategy comprises RANSAC method (M.A.FischlerandR.C.Bolles, " Randomsampleconsensus:Aparadigmformodelfittingwithapplic ationtoimageanalysisandautomatedcartog-raphy, " Commun.ACM, vol.24, no.6, pp.381 – 395, Jun.1981), ARHV method (P.H.S.TorrandA.Zisserman, " MLESAC:Anewrobustestimatorwithapplicationtoestimatingima gegeometry, " Comput.Vis.ImageUnder-stand., vol.78, no.1, pp.138 – 156, etc. Apr.2000) method and the VFC method (J.Ma of parameter model is depended on, J.Zhao, J.Tian, A.L.Yuille, andZ.Tu, " Robustpointmatchingviavectorfieldconsensus, " IEEETrans.ImageProcess., vol.23, no.4, pp.1706 – 1721, Apr.2014.), GS method (H.LiuandS.Yan, " Commonvisualpatterndiscoveryviaspatiallycoher-entcorresp ondence, " inProc.IEEEConf.Comput.Vis.PatternRecog., Jun.2010, pp.1609 – 1616.) and ICF method (X.LiandZ.Hu, " Rejectingmismatchesbycorrespondencefunction, " Int.J.Comput.Vis., vol.89, no.1, pp.1 – 17, Aug.2010). wait the method based on nonparametric model.

Although these methods obtain successfully in a lot of fields, but in the picture containing the localized distortion caused because of visual angle change in a large number and picture material more complicated time, the initial matching point pair of a lot of mistake can be obtained after preliminary matches, when error rate exceedes certain proportion, these methods just effectively can not remove mistake.Therefore need badly and a kind ofly to initial matching error rate, there is matching process compared with strong robustness.

Summary of the invention

For prior art defect, the present invention proposes a kind of based on local linear migration and the Image Feature Matching technical scheme of affined transformation.

For achieving the above object, the technical solution used in the present invention is a kind ofly to comprise the following steps based on local linear migration and the Image Feature Matching method of affined transformation,

Step 1, sets up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling, realizes as follows,

For the affined transformation between image to be matched, set up conversion mathematical model as follows,

y＝t(x)＝Ax+o

Wherein, if two images to be matched are image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents affine transformation relationship, A is the affine matrix of 2 × 2, and o is the matrix of 2 × 1, represents the translation between image to be matched;

If known one group of initial matching point centering, on image a, point set is X={x ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, calculating wherein n-th pair of match point is the correct posterior probability p mated _nthere is following posterior probability mathematical model,

Wherein, γ and σ is model parameter, and e is mathematics constant, and b is default coefficient;

Step 2, according to point set X={x ₁..., x _n} ^tand Y={y ₁..., y _n} ^tsolving model parameter, comprises following sub-step,

Step 2.1 is each match point x _n, n=1 ..., N, search for nearest K neighbours' match point respectively, K gets preset value;

Step 2.2, according to the Search Results of step 2.1, adopts least square method to solve the weight matrix W that dimension is N × N;

Step 2.3, by optimization method solving model parameter s, R, o, γ, σ, comprises following sub-step,

Step 2.3.1, initialization, comprises and makes γ=γ ₀, A=I _{2 × 2}, o=0, P=I _{n × N}, γ ₀for the preset initial value of γ, make current iteration number of times k=1, adopt following model parameter formulae discovery σ,

Wherein, matrix T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Step 2.3.2, upgrades matrix P, comprises and adopts gained posterior probability mathematical model in step 1, calculates N is respectively correct coupling posterior probability p to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

Step 2.3.3, calculating parameter A, o are as follows,

Adopt following formulae discovery parameter A,

Wherein, matrix by I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are default parameter;

Adopt following formulae discovery parameter o,

o＝μ _y-Aμ _x

Step 2.3.4, according to parameter A, o that step 2.3.3 calculates, recalculates parameter γ, σ is as follows,

Adopt following formulae discovery parameter γ,

Adopt model parameter formulae discovery σ in step 2.3.1;

Step 2.3.5, differentiates the condition of convergence, comprises and calculates current parameter L, when meeting k=k _maxor (L-L _old)/L _old≤ ε, finishing iteration, k _maxfor maximum iteration time, ε is convergence threshold; Otherwise k=k+1, returns step 2.3.2; The computing formula of described parameter L is as follows,

Wherein, L _oldrepresent the last L calculated;

Step 3, calculates initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point, realizes as follows,

The model parameter solved in described step 2.3 is substituted into posterior probability mathematical model described in step 1, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is default judgment threshold.

And step 2.2 comprises following sub-step,

Step 2.2.1, as match point x _jnot the match point x searched in described step 2.1 _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Step 2.2.2, constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows,

The present invention is also corresponding to be provided a kind of and comprises with lower module based on local linear migration and the Image Feature Matching system of affined transformation,

Model construction module, for setting up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling, realizes as follows,

For the affined transformation between image to be matched, set up conversion mathematical model as follows,

y＝t(x)＝Ax+o

Wherein, if two images to be matched are image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents affine transformation relationship, A is the affine matrix of 2 × 2, and o is the matrix of 2 × 1, represents the translation between image to be matched;

If known one group of initial matching point centering, on image a, point set is X={x ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, calculating wherein n-th pair of match point is the correct posterior probability p mated _nthere is following posterior probability mathematical model,

Wherein, γ and σ is model parameter, and e is mathematics constant, and b is default coefficient;

Parametric solution module, for according to point set X={x ₁..., x _n} ^tand Y={y ₁..., y _n} ^tsolving model parameter, comprises following submodule,

Neighbours' match point submodule, for being each match point x _n, n=1 ..., N, search for nearest K neighbours' match point respectively, K gets preset value;

Weight matrix submodule, for the Search Results according to neighbours' match point submodule, adopts least square method to solve the weight matrix W that dimension is N × N;

Optimization submodule, for by optimization method solving model parameter s, R, o, γ, σ, comprises with lower unit,

Initialization unit, for making γ=γ ₀, A=I _{2 × 2}, o=0, P=I _{n × N}, γ ₀for the preset initial value of γ, make current iteration number of times k=1, adopt following model parameter formulae discovery σ,

Wherein, matrix T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Updating block, for upgrading matrix P, comprising and adopting gained posterior probability mathematical model in model construction module, calculates N is respectively correct coupling posterior probability p to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

First parameter calculation unit, as follows for calculating parameter A, o,

Adopt following formulae discovery parameter A,

Wherein, matrix by I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are default parameter;

Adopt following formulae discovery parameter o,

o＝μ _y-Aμ _x

Second parameter calculation unit, for the parameter A, the o that calculate according to the first parameter calculation unit, recalculates parameter γ, σ is as follows,

Adopt following formulae discovery parameter γ,

Adopt model parameter formulae discovery σ in initialization unit;

Iteration judging unit, for differentiating the condition of convergence, comprising and calculating current parameter L, when meeting k=k _maxor (L-L _old)/L _old≤ ε, finishing iteration, k _maxfor maximum iteration time, ε is convergence threshold; Otherwise, k=k+1, order updating block works; The computing formula of described parameter L is as follows,

Wherein, L _oldrepresent the last L calculated;

Judged result module, for calculating initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point, realizes as follows,

The model parameter solved in described optimization submodule is substituted into posterior probability mathematical model described in model construction module, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is default judgment threshold.

And weight matrix submodule comprises with lower unit,

Initial assignment unit, for working as match point x _jnot the match point x searched in described neighbours' match point submodule _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Solve unit, for constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows,

The present invention has the following advantages:

1, the present invention is directed to Image Feature Matching and propose a kind of unified maximum likelihood framework.Compared with the parameter model generally used, this framework can process nonparametric model situation, and range of application is wider.

2, the present invention construct a kind of local geometric constraint transformation model is optimized.This constraint saves the partial structurtes between adjacent feature point, therefore can be used to process the situation containing a large amount of erroneous matching, has stronger robustness.

Embodiment

First this method is treated matching image affined transformation and is carried out mathematical modeling, and the coupling of the mistake then tentatively set up in a series of matching double points by removal degree sets up correct coupling.This method utilizes Bayes's maximal possibility estimation of band hidden variable to carry out mathematical modeling.Meanwhile, have also been constructed a kind of geometrical constraint retaining partial structurtes between adjacent feature point, even if when there is a large amount of erroneous matching in preliminary matches, still keep good robustness.

Below by embodiment, technical solution of the present invention is further described.

The flow process of embodiment of the present invention institute supplying method mainly comprises 3 steps:

Step 1, sets up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling;

For the affined transformation between image to be matched, set up conversion mathematical model as follows:

y＝t(x)＝Ax+o

Wherein, x and y is the coordinate vector of pixel, namely set two images to be matched to comprising as image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents affine transformation relationship, A is the affine matrix of 2 × 2, and o is the matrix of 2 × 1, represents the translation between image to be matched;

Wrong unavoidably owing to adopting images match of the prior art to carry out initial matching gained matching double points, the object of the invention is removing wherein mistake, is then X={x for point set on known one group of initial matching point centering image a ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, its mid point x ₁corresponding matching point y ₁, by that analogy, some x _ncorresponding matching point y _n, comprise N to initial matching point pair, wherein n-th pair of matching double points is the correct posterior probability p mated _ncan be obtained by following posterior probability calculated with mathematical model:

Wherein, γ and σ is model parameter, and e is mathematics constant, and coefficient b can be specified in advance by those skilled in the art, or adopts variable element, and embodiment adopts 2/5, namely

Step 2, according to known initial matching point pair, i.e. point set X={x ₁..., x _n} ^t, Y={y ₁..., y _n} ^t, solve the parameter of two models in described step 1; Comprise following sub-step:

Step 2.1 is each match point x _n, n=1 ..., N, adopt k-d to set association search method search K the neighbours match point nearest with it respectively, when specifically implementing, those skilled in the art can preset the value of K voluntarily, and in the present embodiment, K gets 15;

Step 2.2, according to the Search Results of described step 2.1, adopts least square method to solve the weight matrix W that dimension is N × N;

In embodiment, weight matrix W solves as follows,

Step 2.2.1, as match point x _jnot the match point x searched in described step 2.1 _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Step 2.2.2, constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows:

Step 2.3, solves unknown mathematical model parameter by optimization method: A, o, γ, σ;

The solution procedure of mathematical model parameter A, o, γ, σ unknown in embodiment is as follows:

Step 2.3.1, initiation parameter; Make parameter γ=γ ₀(when specifically implementing, those skilled in the art can the initial value γ of sets itself γ ₀, embodiment gets γ ₀=0.9), matrix A=I _{2 × 2}(namely dimension be 2 × 2, element be all 1 unit matrix), parameter o=0, matrix P=I _{n × N}(namely dimension to be N × N, element be all 1 unit matrix), current iteration number of times k=1, adopts following model parameter formulae discovery σ:

Wherein, matrix T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Step 2.3.2, upgrades matrix P:

Adopt posterior probability mathematical model described in step 1, calculate the 1st ..., N is respectively the posterior probability p of correct coupling to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

Step 2.3.3, calculating parameter A, o;

Adopt following formulae discovery parameter A:

Wherein, each matrix i _{n × 1}namely dimension to be N × 1, element be all 1 unit matrix, by I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are a default parameter, and during concrete enforcement, the predeterminable value of those skilled in the art, gets 100 in the present embodiment;

Adopt following formulae discovery parameter o:

o＝μ _y-Aμ _x

Step 2.3.4, according to parameter A, o that step 2.3.3 calculates, can recalculate parameter γ, σ based on current t (x);

Adopt following formulae discovery parameter γ:

Adopt model parameter formulae discovery σ described in step 2.3.1;

Step 2.3.5, differentiates the condition of convergence:

Calculate current parameter L, when meeting k=k _maxor ⁽l-L _old)/L _old≤ ε, finishing iteration; Otherwise, namely work as k<k _maxand (L-L _old)/L _old> ε, k=k+1, get back to step 2.3.2, and when specifically implementing, those skilled in the art can free default maximum iteration time k _maxvalue, the value of the present embodiment is 50;

Wherein, ε is convergence threshold, and during concrete enforcement, the predeterminable value of those skilled in the art, such as, get 0.0001, L _oldrepresent the last L performing step 2.3.5 and calculate, when performing step 2.3.5 first, can L be made _oldbe a larger initial value, 6 powers of such as 10, perform the result of calculation that can adopt performed step 2.3.5 last time afterwards.The computing formula of L is as follows:

Step 3, calculates initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point;

The parameter solved in described step 2.3 is substituted into posterior probability mathematical model described in step 1, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is judgment threshold, and when specifically implementing, those skilled in the art can the value of free default judgment threshold, gets 0.5 in the present embodiment.

During concrete enforcement, above flow process can adopt software mode to realize automatically running.Modular mode can also be adopted to provide corresponding system, and the embodiment of the present invention is also corresponding to be provided a kind of and comprises with lower module based on local linear migration and the Image Feature Matching system of affined transformation,

Model construction module, for setting up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling, realizes as follows,

For the affined transformation between image to be matched, set up conversion mathematical model as follows,

y＝t(x)＝Ax+o

Wherein, if two images to be matched are image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents affine transformation relationship, A is the affine matrix of 2 × 2, and o is the matrix of 2 × 1, represents the translation between image to be matched;

If known one group of initial matching point centering, on image a, point set is X={x ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, calculating wherein n-th pair of match point is the correct posterior probability p mated _nthere is following posterior probability mathematical model,

Wherein, γ and σ is model parameter, and e is mathematics constant, and b is default coefficient;

Parametric solution module, for according to point set X={x ₁..., x _n} ^tand Y={y ₁..., y _n} ^tsolving model parameter, comprises following submodule,

Neighbours' match point submodule, for being each match point x _n, n=1 ..., N, search for nearest K neighbours' match point respectively, K gets preset value;

Weight matrix submodule, for the Search Results according to neighbours' match point submodule, adopts least square method to solve the weight matrix W that dimension is N × N;

Optimization submodule, for by optimization method solving model parameter s, R, o, γ, σ, comprises with lower unit,

Initialization unit, for making γ=γ ₀, A=I _{2 × 2}, o=0, P=I _{n × N}, γ ₀for the preset initial value of γ, make current iteration number of times k=1, adopt following model parameter formulae discovery σ,

Wherein, matrix T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Updating block, for upgrading matrix P, comprising and adopting gained posterior probability mathematical model in model construction module, calculates N is respectively correct coupling posterior probability p to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

First parameter calculation unit, as follows for calculating parameter A, o,

Adopt following formulae discovery parameter A,

Wherein, matrix by I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are default parameter;

Adopt following formulae discovery parameter o,

o＝μ _y-Aμ _x

Second parameter calculation unit, for the parameter A, the o that calculate according to the first parameter calculation unit, recalculates parameter γ, σ is as follows,

Adopt following formulae discovery parameter γ,

Adopt model parameter formulae discovery σ in initialization unit;

Iteration judging unit, for differentiating the condition of convergence, comprising and calculating current parameter L, when meeting k=k _maxor (L-L _old)/L _old≤ ε, finishing iteration, k _maxfor maximum iteration time, ε is convergence threshold; Otherwise, k=k+1, order updating block works; The computing formula of described parameter L is as follows,

Wherein, L _oldrepresent the last L calculated;

Judged result module, for calculating initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point, realizes as follows,

The model parameter solved in described optimization submodule is substituted into posterior probability mathematical model described in model construction module, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is default judgment threshold.

Further, weight matrix submodule comprises with lower unit,

Initial assignment unit, for working as match point x _jnot the match point x searched in described neighbours' match point submodule _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Solve unit, for constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows,

Choose the contrast that RANSAC, ICF, GS method and the present invention carry out images match.Comparing result is as following table, and wherein accuracy refers in the matching double points that method finally provides it is the ratio of correct matching double points; Missing rate refers to that method is judged as correct matching double points the ratio that error matching points is right in screening process.Can see that this method used time is the shortest, accuracy rate is the highest, and missing rate is minimum.

Method Contrast on effect table

Method	Time	Accuracy	Missing rate
				RANSAC	15.36	75.32％	19.54％
ICF	1.66	46.34％	8.66％
				GS	0.64	86.87％	33.29％
The present invention	0.34	99.11％	2.92％

Specific embodiment described herein is only to the explanation for example of the present invention's spirit.Those skilled in the art can make various amendment or supplement or adopt similar fashion to substitute to described specific embodiment, but can't depart from spirit of the present invention or surmount the scope that appended claims defines.

Claims (4)

1., based on local linear migration and the Image Feature Matching method of affined transformation, it is characterized in that: comprise the following steps,

Step 1, sets up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling, realizes as follows,

For the rigidity geometric transformation between image to be matched, set up conversion mathematical model as follows,

y＝t(x)＝sRx+o

Wherein, if two images to be matched are image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents rigidity geometric transform relation, s represents the dimension scale between image to be matched, and R is the matrix of 2 × 2, represents the rotation between image to be matched, o is the matrix of 2 × 1, represents the translation between image to be matched;

If known one group of initial matching point centering, on image a, point set is X={x ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, calculating wherein n-th pair of match point is the correct posterior probability p mated _nthere is following posterior probability mathematical model,

$p_n=\frac{{\mathrm{\&gamma;e}}^{-\frac{\left|\right|y_n-t\left(x_n\right)|{|}^2}{2{\mathrm{\&sigma;}}^2}}}{{\mathrm{\&gamma;e}}^{-\frac{\left|\right|y_n-t\left(x_n\right)|{|}^2}{2{\mathrm{\&sigma;}}^2}}+{\mathrm{b\&pi;\&sigma;}}^2(1-\mathrm{\&gamma;})},n=1,...N$

Wherein, γ and σ is model parameter, and e is mathematics constant, and b is default coefficient;

Step 2, according to point set X={x ₁..., x _n} ^tand Y={y ₁..., y _n} ^tsolving model parameter, comprises following sub-step,

Step 2.1 is each match point x _n, n=1 ..., N, search for nearest K neighbours' match point respectively, K gets preset value;

Step 2.2, according to the Search Results of step 2.1, adopts least square method to solve the weight matrix W that dimension is N × N;

Step 2.3, by optimization method solving model parameter s, R, o, γ, σ, comprises following sub-step,

Step 2.3.1, initialization, comprises and makes γ=γ ₀, R=I _{2 × 2}, o=0, P=I _{n × N}, γ ₀for the preset initial value of γ, make current iteration number of times k=1, adopt following model parameter formulae discovery σ,

${\mathrm{\&sigma;}}^2=\frac{tr\left({\left(Y-T\right)}^{T}P\right(Y-T\left)\right)}{2\&CenterDot;tr\left(P\right)}$

Wherein, T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Step 2.3.2, upgrades matrix P, comprises and adopts gained posterior probability mathematical model in step 1, calculates N is respectively correct coupling posterior probability p to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

Step 2.3.3, calculating parameter s, R, o are as follows,

Adopt following formulae discovery parameter s,

$s=\frac{tr\left({\left({\hat{Y}}^{T}P\hat{X}\right)}^{T}R\right)}{tr\left({\hat{X}}^{T}P\hat{Y}\right)+2{\mathrm{\&lambda;\&sigma;}}^{2}tr\left(X^{T}QX\right)}$

Wherein, matrix $\hat{X}=X-I_{N\&times;1}{\mathrm{\&mu;}}_x^T,$ $\hat{Y}=Y-I_{N\&times;1}{\mathrm{\&mu;}}_y^T,$ ${\mathrm{\&mu;}}_x=\frac{1}{tr\left(P\right)}{X}^T{\mathrm{PI}}_{N\&times;1},$ ${\mathrm{\&mu;}}_y=\frac{1}{tr\left(P\right)}{Y}^T{\mathrm{PI}}_{N\&times;1},$ By I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are default parameter;

Adopt following formulae discovery parameter R,

R＝UDV ^T

Wherein, D=diag (1, det (UV ^t)), the determinant of det () representing matrix, matrix U and V are obtained by svd;

Adopt following formulae discovery parameter o,

o＝μ _y-sRμ _x

Step 2.3.4, the parameter s calculated according to step 2.3.3, R, o, recalculate parameter γ, σ be as follows,

Adopt following formulae discovery parameter γ,

$\mathrm{\&gamma;}=\frac{tr\left(P\right)}{N}$

Adopt model parameter formulae discovery σ in step 2.3.1;

Step 2.3.5, differentiates the condition of convergence, comprises and calculates current parameter L, when meeting k=k _maxor (L-L _old)/L _old≤ ε, finishing iteration, k _maxfor maximum iteration time, ε is convergence threshold; Otherwise k=k+1, returns step 2.3.2; The computing formula of described parameter L is as follows,

$\begin{array}{c}L=-\frac{1}{2{\mathrm{\&sigma;}}^2}{\&Sigma;}_{n=1}^N{p}_n\left|\right|y_n-t\left(x_n\right)|{|}^2-\left(ln\frac{{\mathrm{\&sigma;}}^2}{\mathrm{\&gamma;}}\right){\mathrm{\&Sigma;}}_{n=1}^N{p}_n+ln(1-\mathrm{\&gamma;}){\mathrm{\&Sigma;}}_{n=1}^N(1-p_n)\\ -\mathrm{\&lambda;}{\&Sigma;}_{i=1}^N{p}_i\left|\right|t\left(x_i\right)-{\mathrm{\&Sigma;}}_{j=1}^N{W}_{ij}{x}_j|{|}^2\end{array}$

Wherein, L _oldrepresent the last L calculated;

Step 3, calculates initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point, realizes as follows,

The model parameter solved in described step 2.3 is substituted into posterior probability mathematical model described in step 1, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is default judgment threshold.

2., according to claim 1 based on local linear migration and the Image Feature Matching method of affined transformation, it is characterized in that: step 2.2 comprises following sub-step,

Step 2.2.1, as match point x _jnot the match point x searched in described step 2.1 _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Step 2.2.2, constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows,

$E\left(W\right)={\mathrm{\&Sigma;}}_{i=1}^N\left|\right|x_i-{\mathrm{\&Sigma;}}_{j=1}^N{W}_{ij}{x}_j|{|}^2.$

3., based on local linear migration and the Image Feature Matching system of affined transformation, it is characterized in that: comprise with lower module,

Model construction module, for setting up the corresponding model of posterior probability that the corresponding model of geometric transformation and matching double points between image to be matched are correct coupling, realizes as follows,

For the rigidity geometric transformation between image to be matched, set up conversion mathematical model as follows,

y＝t(x)＝sRx+o

Wherein, if two images to be matched are image a and image b, x and y is the coordinate vector of pixel on image a and image b respectively, t (x) represents rigidity geometric transform relation, s represents the dimension scale between image to be matched, and R is the matrix of 2 × 2, represents the rotation between image to be matched, o is the matrix of 2 × 1, represents the translation between image to be matched;

If known one group of initial matching point centering, on image a, point set is X={x ₁..., x _n} ^t, on image b, corresponding point set is Y={y ₁..., y _n} ^t, calculating wherein n-th pair of match point is the correct posterior probability p mated _nthere is following posterior probability mathematical model,

$p_n=\frac{{\mathrm{\&gamma;e}}^{-\frac{\left|\right|y_n-t\left(x_n\right)|{|}^2}{2{\mathrm{\&sigma;}}^2}}}{{\mathrm{\&gamma;e}}^{-\frac{\left|\right|y_n-t\left(x_n\right)|{|}^2}{2{\mathrm{\&sigma;}}^2}}+{\mathrm{b\&pi;\&sigma;}}^2(1-\mathrm{\&gamma;})},n=1,...N$

Wherein, γ and σ is model parameter, and e is mathematics constant, and b is default coefficient;

Parametric solution module, for according to point set X={x ₁..., x _n} ^tand Y={y ₁..., y _n} ^tsolving model parameter, comprises following submodule,

Neighbours' match point submodule, for being each match point x _n, n=1 ..., N, search for nearest K neighbours' match point respectively, K gets preset value;

Weight matrix submodule, for the Search Results according to neighbours' match point submodule, adopts least square method to solve the weight matrix W that dimension is N × N;

Optimization submodule, for by optimization method solving model parameter s, R, o, γ, σ, comprises with lower unit,

Initialization unit, for making γ=γ ₀, R=I _{2 × 2}, o=0, P=I _{n × N}, γ ₀for the preset initial value of γ, make current iteration number of times k=1, adopt following model parameter formulae discovery σ,

${\mathrm{\&sigma;}}^2=\frac{tr\left({\left(Y-T\right)}^{T}P\right(Y-T\left)\right)}{2\&CenterDot;tr\left(P\right)}$

Wherein, T=(t (x ₁) ..., t (x _n)) ^t, matrix trace is asked in tr () expression;

Updating block, for upgrading matrix P, comprising and adopting gained posterior probability mathematical model in model construction module, calculates N is respectively correct coupling posterior probability p to matching double points ₁..., p _n, make P=diag (p ₁..., p _n), diag represents diagonal matrix;

First parameter calculation unit, as follows for calculating parameter s, R, o,

Adopt following formulae discovery parameter s,

$s=\frac{tr\left({\left({\hat{Y}}^{T}P\hat{X}\right)}^{T}R\right)}{tr\left({\hat{X}}^{T}P\hat{Y}\right)+2{\mathrm{\&lambda;\&sigma;}}^{2}tr\left(X^{T}QX\right)}$

Wherein, matrix $\hat{X}=X-I_{N\&times;1}{\mathrm{\&mu;}}_x^T,$ $\hat{Y}=Y-I_{N\&times;1}{\mathrm{\&mu;}}_y^T,$ ${\mathrm{\&mu;}}_x=\frac{1}{tr\left(P\right)}{X}^T{\mathrm{PI}}_{N\&times;1},$ ${\mathrm{\&mu;}}_y=\frac{1}{tr\left(P\right)}{Y}^T{\mathrm{PI}}_{N\&times;1},$ By I _{n × N}omission is designated as I, matrix Q=(I-W) ^tp (I-W), λ are default parameter;

Adopt following formulae discovery parameter R,

R＝UDV ^T

Wherein, D=diag (1, det (UV ^t)), the determinant of det () representing matrix, matrix U and V are obtained by svd; Adopt following formulae discovery parameter o,

o＝μ _y-sRμ _x

Second parameter calculation unit, for calculate according to the first parameter calculation unit parameter s, R, o, recalculate parameter γ, σ be as follows,

Adopt following formulae discovery parameter γ,

$\mathrm{\&gamma;}=\frac{tr\left(P\right)}{N}$

Adopt model parameter formulae discovery σ in initialization unit;

Iteration judging unit, for differentiating the condition of convergence, comprising and calculating current parameter L, when meeting k=k _maxor (L-L _old)/L _old≤ ε, finishing iteration, k _maxfor maximum iteration time, ε is convergence threshold; Otherwise, k=k+1, order updating block works; The computing formula of described parameter L is as follows,

$\begin{array}{c}L=-\frac{1}{2{\mathrm{\&sigma;}}^2}{\&Sigma;}_{n=1}^N{p}_n\left|\right|y_n-t\left(x_n\right)|{|}^2-\left(ln\frac{{\mathrm{\&sigma;}}^2}{\mathrm{\&gamma;}}\right){\mathrm{\&Sigma;}}_{n=1}^N{p}_n+ln(1-\mathrm{\&gamma;}){\mathrm{\&Sigma;}}_{n=1}^N(1-p_n)\\ -\mathrm{\&lambda;}{\&Sigma;}_{i=1}^N{p}_i\left|\right|t\left(x_i\right)-{\mathrm{\&Sigma;}}_{j=1}^N{W}_{ij}{x}_j|{|}^2\end{array}$

Wherein, L _oldrepresent the last L calculated;

Judged result module, for calculating initial matching point to the posterior probability for correct coupling, and according to right the correcting errors of threshold decision initial matching point, realizes as follows,

The model parameter solved in described optimization submodule is substituted into posterior probability mathematical model described in model construction module, calculates the posterior probability that n-th pair of matching double points is correct coupling; Work as p _nduring>=threshold, then think that n-th pair of match point is correct coupling; Work as p _nduring <threshold, then think that n-th pair of match point is the coupling of mistake, wherein threshold is default judgment threshold.

4., according to claim 3 based on local linear migration and the Image Feature Matching system of affined transformation, it is characterized in that: weight matrix submodule comprises with lower unit,

Initial assignment unit, for working as match point x _jnot the match point x searched in described neighbours' match point submodule _ik neighbours' match point for the moment, then by W _ijbe set to 0, wherein match point x _jwith match point x _ifor the match point in point set X, and i, j=1 ..., N, W _ijrepresent the element of weight matrix W i-th row jth row;

Solve unit, for constraint condition under, adopt least square method to solve and obtain making following cost function E (W) get the weight matrix W of minimum value, cost function E (W) is as follows,

$E\left(W\right)={\mathrm{\&Sigma;}}_{i=1}^N\left|\right|x_i-{\mathrm{\&Sigma;}}_{j=1}^N{W}_{ij}{x}_j|{|}^2.$