CN101567051B - Image matching method based on characteristic points - Google Patents

Image matching method based on characteristic points Download PDF

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Publication number
CN101567051B
CN101567051B CN2009100524538A CN200910052453A CN101567051B CN 101567051 B CN101567051 B CN 101567051B CN 2009100524538 A CN2009100524538 A CN 2009100524538A CN 200910052453 A CN200910052453 A CN 200910052453A CN 101567051 B CN101567051 B CN 101567051B
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sign indicating
point
corner sign
ring type
similarity
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CN2009100524538A
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CN101567051A (en
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魏二岭
杨夙
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复旦大学
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Abstract

The invention belongs to the technical fields of mode indentifying and image processing, and relates to an image matching method based on characteristic points. The image matching method based on characteristic points defines an annular corner code model according to the K neighboring structure of the characteristic points and applies the model to point mode matching. Similar annular corner codes not only describe the similarity of the space structures of two characteristic points, but also can be used for local mapping estimation. The similarity of the two characteristic points is confirmed by the similar length of the largest similar annular corner codes associated with the two characteristic points. The image matching method based on characteristic points carries out structure matching according to the similarity of the local space structures of the characteristic points, and uses the local mapping and clustering to carry out optimization matching. The optimization matching result is used for optimizing the mapping estimation. The method can be used for the image matching based on the characteristic points, image jointing and embedding, motion tracing, information fusion of medical single-mode and multi-mode images, as well as image searching based on contents.

Description

A kind of method for registering images based on unique point

Technical field

The invention belongs to pattern-recognition and technical field of image processing, be specifically related to a kind of method for registering images based on unique point.This method can be used for the image registration based on unique point, image mosaic with inlay motion tracking, the information fusion of medical science single mode and multi-modality images, and CBIR etc.

Background technology

Point Pattern Matching is appreciated that to be the images match based on characteristic, estimates from two points to be matched concentrated search corresponding relation and to the mapping between the point set.Point Pattern Matching is widely used in remote sensing (image mosaic, image mosaic), medical image (state of an illness diagnosis and tracking) and computer vision (target or scene Recognition, motion tracking).

Though a lot of in recent years outstanding Point Pattern Matching algorithms are suggested, almost there is not a kind of algorithm on computing time and precision, to satisfy application requirements simultaneously.Present disclosed algorithm is broadly divided into 5 types: clustering algorithm (document that sees reference [1] [2]); Shape contextual algorithms (document that sees reference [3]); Loose labeling algorithm (document that sees reference [4] [5]), progressively optimized Algorithm (document that sees reference [6]) is schemed matching algorithm (document that sees reference [7]).

Wherein,

Clustering algorithm hypothesis transformation model is similarity transformation; Only need two pairs of match points because estimate similarity transformation; Algorithm is at first concentrated from two points to be matched and selected two pairs of points to make up, and estimates conversion coefficient then, and is right with other points that possibly mate of conversion checking of estimating.Because also is correct with the point of correct match to the conversion of estimating, carry out cluster with this conversion, then can might gather such to institute by correct match point, and if the point of correct match many, class also can be very big accordingly; On the contrary, if with the conversion to estimating of the point of erroneous matching, then this conversion is wrong certainly, and it is exactly randomness that erroneous conversions has characteristics.Erroneous matching that is to say that making sorter with erroneous conversions carries out cluster, then only converges to seldom point mostly at random.Algorithm carries out cluster according to this character to two feature point sets, and in the cluster result, maximum kind will be as corresponding relation output to be used for subsequent treatment.The defective of this type algorithm is that computation complexity is high, and is consuming time, and if two feature point set difference in size very big, algorithm performance also can non-constant.Hough transformation was used to pre-service afterwards with speed-up computation, yet coupling remains a kind of work very consuming time, and was and very many at noise spot, and perhaps under two very large situation of point set difference in size, algorithm not necessarily guarantees to find out best correspondence.

The shape contextual algorithms also is to study the Point Pattern Matching technology of comparative heat in the last few years.The shape context-descriptive point concentrate other unique points space distribution information of certain unique point relatively.The unique point of mating in the same shape of two width of cloth images has the shapes similar context, and each unique point is corresponding direction histogram is to represent other unique points space constraint of oneself relatively.This algorithm can solve the Point Pattern Matching of non-rigid transformation, and most of similar algorithm use SPL estimates conversion, has dependence yet the problem of this type algorithm is sampling to unique point; Image background can not be too complicated, and the test of various algorithms is used for simple form fit also mostly based on generated data; Algorithm performance is relatively good; But the computation complexity of this type algorithm is than higher, and each unique point all need utilize every other point to calculate hereinafter in shape, therefore in complex scene; Under the situation that the object of identification relatively is difficult to again cut apart, the poor performance of algorithm.

Loose labeling algorithm defines a kind of probability distribution on transformation relation; Be optimized with discrete logarithm then, algorithm is actually the process of an iteration, since a thicker coupling; Progressively upgrade the matching probability matrix then, this algorithm is easy to converge on local extremum.

Progressively optimized Algorithm also is to utilize global characteristic point to make up a compatible function, and through upgrading compatible function to solve correspondence, this algorithm has identical defective with loose algorithm.Two types of algorithms are bigger at feature point set, and algorithm performance is all poor under the many again situation of noise, and can not guarantee that algorithm can restrain under various situation.

The figure matching algorithm is the Point Pattern Matching algorithm of the comparative heat of recent researches.Through two point sets to be matched are built into weighted graph, attributed relational graph etc. are searched for compatible part then in two figure, and the part of promptly mating is probably perhaps carried out the subgraph coupling, perhaps shines upon coupling fully, guarantees that majorized function is minimum.This type algorithm can be summed up as the graph search problem, is the difficult problem of a NP, can not guarantee various survey examples are all separated.A lot of algorithms also all are to utilize majorized function to estimate; Obtain more excellent separating rather than optimum solution; Another defective of figure coupling is to handle two point sets that all contain much noise point, except computing time is long, so this algorithm also is difficult to obtain rational corresponding relation.

The list of references relevant with the present invention has:

[1]A.Goshtasby.Description?and?discrimination?of?planar?shapes?using?shape?matrices.IEEE?Trans.Pattern?Analysis?and?Machine?Intelligence,7:738-743,1985.

[2]S.H.Chang,F.H.Cheng,W.H.Hsu,and?G.Z.Wu.Fast?algorithm?for?point?pattern?matching:Invariant?to?translations,rotations?and?scale?changes.Pattern?Recognition,30:311-320,1997.

[3]S.Belongie,J.Malik,and?J.Puzicha,“Shape?Matching?and?Object?Recognition?Using?ShapeContexts,”IEEE?Trans.Pattern?Analysis?and?Machine?Intelligence,vol.24,no.4,pp.509-522,Apr.2002.

[4]W.J.Christmas,J.Kittler,and?M.Petrou,“Structural?Matching?in?Computer?Vision?UsingProbabilistic?Relaxation,”IEEE?Trans.Pattern?Analysis?and?Machine?Intelligence,vol.17,no.8,pp.749-764,Aug.1994.

[5]R.C.Wilson?and?E.R.Hancock,“Structural?Matching?by?Discrete?Relaxation,”IEEE?Trans.Pattern?Analysis?and?Machine?Intelligence,vol.19,no.6,pp.634-648,June?1997.

[6]S.Gold?and?A.Rangarajan,“A?Graduated?Assignment?Algorithm?for?Graph?Matching,”IEEE?Trans.Pattern?Analysis?and?Machine?Intelligence,vol.18,no.4,pp.377-388,Apr.1996.

[7]Tibe’rio?S.Caetano,Terry?Caelli,Fellow,IEEE,Dale?Schuurmans,and?Dante?A.C.Barone.Graphical?Models?and?Point?Pattern?Matching.IEEE?Trans.Pattern?Analysis?and?MachineIntelligence,28:1646-1663,2006.

Summary of the invention

The present invention is intended to overcome the deficiency that exists in the prior art, proposes a kind of method for registering images based on unique point.

The present invention at first proposes a kind of ring type corner sign indicating number model based on unique point k nearest neighbor space structure, the similarity between the sign indicating number of ring type corner is explored and used it for the similarity of the local space of weighing two unique points.Described similar ring type corner sign indicating number has not only been described the similarity of the local space structure between two unique points, can also be used to estimate local map.The sign indicating number modelling of ring type corner is on the similarity transformation basis, and the present invention has carried out loose simultaneously to ring type corner sign indicating number simulated condition, makes matching algorithm have robustness to a certain degree affine and view transformation.Similarity between the unique point by they related maximal phase confirm like the similar length of ring type corner sign indicating number.The present invention at first carries out structure matching according to the similarity of the local space structure of unique point, utilize then match point related local map cluster be optimized coupling.The result of optimization of matching is used to estimate optimum mapping.Aspect the acceleration matching process, the present invention has avoided a large amount of unnecessary computations through set up index in the search volume, thereby guarantees that matching algorithm has also reached when guaranteeing precision near the requirement of application in real time.

The invention discloses a kind of algorithm of Point Pattern Matching fast and effectively, its process flow diagram is seen accompanying drawing 1.Comprise that the step is following rapid:

Step 1): feature extraction; Two images to be matched are carried out feature extraction, and matching algorithm is based on image characteristic point, and coupling extraction in early stage has convergent-divergent; The feature point set of rotation and translation invariance also can have affine and view transformation to a certain degree as input between the point set;

Step 2): similarity is calculated; The ring type corner sign indicating number of the k nearest neighbor structure of extract minutiae and construction feature point; Index is set up in the search volume; Utilize maximal phase between binary chop and the increment matching algorithm calculated characteristics point like ring type corner sign indicating number, and with maximal phase like the similar length of ring type corner as the similarity between two unique points, confirm the unique point that possibly mate when calculating similarity;

Step 3): structure matching; Similarity between the unique point has been described the similarity of the local space structure of unique point in the physical sense; The present invention is according to the similarity between unique point size, and the unique point that possibly mate is divided into three types: unique point class badClass that can not coupling, possibly mate but mate not unique unique point class unknownClass; The unique point class goodClass that possibly mate carries out local map to the match point among the goodClass simultaneously and estimates;

Step 4): optimization of matching, utilize match point among the goodClass related local map carry out cluster, in cluster result, what the definition element was maximum type be maximum kind, the class of element more than second be inferior class greatly.If simultaneously greater than inferior big dvielement number, perhaps maximum kind and the ratio of inferior big type element number are greater than certain threshold value greater than certain threshold value for the maximum kind element number, maximum kind will be estimated optimal transformation as matching result;

Step 5): optimal transformation estimates that the present invention supposes being transformed to except similarity transformation between the image, also has affine and view transformation to a certain degree, and the conversion estimation model is an affined transformation.

Particularly,

Two point sets in the step 1 of the present invention are defined as S and T, and size is respectively n 1And n 2, while defining point P i∈ S, Q j∈ T.

Ring type corner sign indicating number in the described step 2 is defined as:

Shown in accompanying drawing 2, the k nearest neighbor space structure of unique point can pass through a ring type corner representation.For defining ring type corner sign indicating number better, the present invention with the combination at the angle of every limit and its counterclockwise adjacency in the accompanying drawing 2 (a) be defined as a corner sign indicating number (Edge-Angle Code) EAC=(E, θ).Accompanying drawing 2 (a) has K corner sign indicating number (E i, θ i), i=0,1 ..., K-1.Also successively they being linked to be a loop configuration then obtains unique point P by counterclockwise sequential encoding to this K corner sign indicating number cRing type corner sign indicating number, shown in accompanying drawing 2 (b), the present invention defines the length that K is a ring type corner sign indicating number.

The defined ring type of the present invention corner sign indicating number has following characteristic:

1) connectedness: sign indicating number begins to travel through forward from arbitrary corner, can all corner sign indicating numbers be traveled through in order to finish.For ease of expression, the present invention will travel through subscript and be defined as m t, wherein t representes nonnegative integer, and m tRepresent that then t gets surplus result to K, do to guarantee that subscript can not crossed the border when the corner sign indicating number is traveled through like this.

2) space constraint property: the order of corner sign indicating number has been described the restriction relation that the neighbours of unique point are ordered.Corner sign indicating number order ( E m i , θ m i ) , ( E m ( i + 1 ) , θ m ( i + 1 ) ) , ( E m ( i + 2 ) , θ m ( i + 2 ) ) Neighbours' point has been described in space structure With neighbours' point Adjacency in the counterclockwise direction, and with neighbours' point Neighbours' point at interval in the counterclockwise direction.Therefore the corner sign indicating number is analyzed to be equivalent in proper order the restriction relation of neighbours' point on space structure analyzed.

3) equivalence: same unique point can have K different ring type corner sign indicating number, but they have described the same local space structure of unique point all of equal valuely.(a) can know by accompanying drawing 2, and during as first corner sign indicating number, the present invention will obtain different ring type corner sign indicating numbers with different neighbours point, but they are of equal value in the physical sense, have promptly all described identical space structure.The present invention is defined as equivalence to this specific character of ring type corner sign indicating number.

Similar ring type corner sign indicating number definition in the described step 2 is specially:

Shown in accompanying drawing 3; If it is proportional that two ring type corner sign indicating numbers begin to satisfy corresponding sides from a certain opposite side; Corresponding angles are proportional and equal 1; The present invention just says that these two ring type corner sign indicating numbers are similar, and the length of one of them ring type corner sign indicating number is defined as the similar length of similar ring type corner sign indicating number, and the space structure that similar ring type corner sign indicating number is corresponding is defined as similar space structure.Similar ring type corner sign indicating number shown in the accompanying drawing 3 is from (E 0, E ' 0) begin to satisfy following formula:

| E ′ 0 | | E 0 | = | E ′ 1 | | E 1 | = K = | E ′ K - 1 | | E K - 1 |

θ ′ 0 θ 0 = θ ′ 1 θ 1 = K = θ ′ K - 1 θ K - 1 = 1

Be calculation procedure two described similar ring type corner sign indicating numbers, the present invention has also defined the add operation of two adjacent corner sign indicating numbers, is specially:

( E m i , θ m i ) + ( E m ( i + 1 ) , θ m ( i + 1 ) ) = ( E m i , θ m i + θ m ( i + 1 ) )

The result of two adjacent corner sign indicating number add operations still is a corner sign indicating number, and the limit of new corner sign indicating number is the limit of first corner sign indicating number, and the angle of new corner sign indicating number then is the angle sum of two addition corner sign indicating numbers.In fact the physical significance of phase add operation has been represented two adjacent neighbours' points of counter clockwise direction are merged, and is about to second neighbours' point deletion, then the angle of the counterclockwise adjacency in article one limit is upgraded.

Be calculation procedure two described similar ring type corner sign indicating numbers, the present invention has also defined the compare operation of two corner sign indicating numbers, if two corner sign indicating number (E 1, θ 1), (E ' 1, θ ' 1) satisfy

abs ( | E &prime; 1 | / | E 1 | | E &prime; 2 | / | E 2 | - 1.0 ) < &epsiv; abs ( &theta; &prime; 1 &theta; 1 - 1.0 ) < &epsiv;

Wherein ε is the error control factor, approaches 0 positive number, (E 2, θ 2), (E ' 2, θ ' 2) be respectively (E 1, θ 1), (E ' 1, θ ' 1) the corner sign indicating number of next adjacency, the present invention is regulation (E just 1, θ 1), (E ' 1, θ ' 1) compare operation do not need add operation, the physical significance that does not need add operation be the expression by E 1, θ 1, E 2The triangle that constitutes with by E ' 1, θ ' 1, E ' 2The triangle that constitutes is similar.

Described step 2 similarity is calculated and is specially:

(1) set up the search volume, the ring type corner sign indicating number of the k nearest neighbor of extract minutiae and construction feature point, the search volume is that impact point is concentrated the n on the ring type corner sign indicating number of all unique points 2K corner sign indicating number set up index with acceleration search to these corner sign indicating numbers by angle.Synoptic diagram is seen in the middle of the accompanying drawing 4 and the right two table.

(2) confirm object search, the coupling purpose is to find match point.Therefore the single object search is P iRing type corner sign indicating number related each corner sign indicating number, with regard to P i, total K object search (E k, θ k), shown in accompanying drawing 4 left side forms.

(3) confirm search strategy, the present invention is at first to (E k, θ k) utilize the binary search corner sign indicating number (E that angle is identical in the seek error allowed band in the search volume n, θ n), establishing characteristic of correspondence point is Q jIf find, the present invention also can be in finding corner other identical (E of linear search angle in the error allowed band before and after the sign indicating number n, θ n).If (E k, θ k) and (E n, θ n) comparative result do not need add operation, then respectively with (E k, θ k) and (E n, θ n) be the ring type corner sign indicating number L of two equivalences of dot generation P={ A 0, A 1..., A (K-1)And L Q={ B 0, B 1..., B (K-1), A wherein k, B nExpression corner sign indicating number, note E k A, θ k A, E n B, θ n BRepresent A respectively k, B nBian Hejiao.Press their maximal phase of increment matching algorithm calculating then like ring type corner sign indicating number.The increment matching algorithm is following: at first to A 0, B 0Compare operation, if θ 0 A, θ 0 BUnequal, the next corner sign indicating number that the corner sign indicating number that then angle is little is adjacent carries out add operation and generates new corner sign indicating number; If θ 0 A, θ 0 BEquate, then verify θ 0 A, θ 0 BAdjacent corresponding sides (E 0 A, E 0 B) and (E 1 A, E 1 B) whether proportional, if proportional, then with A 0, B 0Be retained in respectively in the similar ring type corner sign indicating number of having tried to achieve (just having begun to be sky), if disproportionate, then with this A 0, B 0Carry out add operation with adjacent separately next corner sign indicating number and generate new corner sign indicating number, next compare operation is carried out in circulation then, and cyclic process is till one of them ring type corner sign indicating number has traveled through.False code is represented as follows:

Suppose t 1And t 2Be two loop variables, t 1=0, t 2=0.Make temp A=A k, temp B=B n, rate=E n B/ E k A, then the increment matching algorithm is following:

while ( t 1 < Kand t 2 < K ) {

if ( &theta; temp A < &theta; temp B ) { t 1 = t 1 + 1 , temp A = temp A + A m ( i + t 1 ) . }

elseif ( &theta; temp A > &theta; tem p B ) { t 2 = t 2 + 1 , temp B = temp B + B m ( j + t 2 ) . }

else {

t 1 = t 1 + 1 , t 2 = t 2 + 1 .

if ( rate = = E B m ( n + t 2 ) / E A m ( k + t 1 ) )

{ Add temp A into L k , add temp B into L n , tem p A = A m ( k + t 1 ) , temp B = B m ( n + t 2 ) . }

else { temp A = temp A + A m ( k + t 1 ) , temp B = temp B + B m ( n + t 2 ) . } } }

Only keep the maximum similar ring type corner sign indicating number of similar length in the search procedure,, all keep when the maximum ring type corner sign indicating number of similar length has when a plurality of.With maximal phase like the similar length of ring type corner sign indicating number as the similarity between two unique points, notice that the similarity of all two unique points not calculated in the search procedure is 0.

(4) the definite unique point that possibly mate, the present invention defines S (A k, B n)={ L k, L nRepresent with E k A, E n BBe the similar ring type corner sign indicating number of initial corresponding sides, L k, L nRepresent similar ring type corner sign indicating number.Make S (A k, B n) similar length be len Ij(k, n), unique point P then i, Q jSimilarity be:

similarity ( P i , Q j ) = max k , n &Element; [ 0 , K - 1 ] len ij ( k , n )

If Q j *Satisfy

Q j * = arg max O j &Element; T similarity ( P i , Q j )

The present invention just thinks Q j *Be P iPossible match point, P in the computation process iHave a plurality of Q that possibly mate j *, all keep.

Step 3 structure matching of the present invention is specially:

If P iThe Q that possibly mate j *There is NO, then to possibility match point (P i, Q * j) be classified as follows:

( P i , Q * j ) &Element; badClass if similarity ( P i , Q * j ) < 3 unknownClass if similarity ( P i , Q * j ) &GreaterEqual; 3 andNO > 1 goodClass if similarity ( P i , Q * j ) &GreaterEqual; 3 andNO = 1

Class condition is explained as follows: (the P among the badClass i, Q * j) similarity all less than 3, when K is bigger,, (P is described like K=15 i, Q * j) the local space textural difference very big, P iOr Q * jPossibly be noise spot, the possibility of coupling is very little; (P among the unknownClass i, Q * j) similarity all more than or equal to 3, when K is bigger,, (P is described like K=15 i, Q * j) the local space texture ratio more similar, but P iThe Q that possibly mate * jMore than one, this explanation only can't be confirmed P through the similarity of local space structure iWith which Q * jCoupling needs subsequent authentication to confirm; (P among the goodClass i, Q * j) similarity all more than or equal to 3, when K is bigger,, (P is described like K=15 i, Q * j) the local space texture ratio more similar, and P iThe Q that possibly mate * jHas only one, (P i, Q * j) possibility of correct match is very big, and similarity is big more, explains that the local space structure is similar more, the possibility of coupling is big more.

According to above-mentioned analysis, the present invention is with (the P among the goodClass i, Q * j) as the result of structure matching, the present invention simultaneously is also to the (P among the goodClass i, Q * j) carry out the local similar conversion and estimate,, note in local similar conversion estimation procedure (P i, Q * j) the one group respective neighbours point related with them make up and then estimate one group of local similar conversion, if (P i, Q * j) similarity be ls, then can estimate the conversion of ls group local similar, matching algorithm is got the average of ls group local similar conversion as the final local similar conversion of estimating.

Described step 4, optimization of matching is specially:

(1) to (P among the goodClass i, Q * j) sort by similarity is descending;

(2) to arranging (the P of preface i, Q * j) scan, if (P i, Q * j) not by the cluster mistake, then with (P i, Q * j) type of being center C i, (P i, Q * j) related local map T iAs sorter, do not verified by the possible match point of cluster and related respective neighbours point among goodClass and the unknownClass.Cluster process is following: at first with (P i, Q * j) related respective neighbours point is included into such, because sorter T iCome out by these respective neighbours point estimation; Utilize among other goodClass of sorter checking and the unknownClass the not possible match point of cluster and related respective neighbours point then; If the point of being verified is to satisfying sorter; Then it is carried out mark, no longer carry out subsequent treatment and it is included into such, otherwise it is ignored.If a pair of point that wherein need verify is for (P Q), establishes P through T iPoint after the mapping is T i(P), if T i(P) can accept in error range with the Euclidean distance of Q, the present invention thinks that then (P Q) meets T i, with (P Q) is classified as C iBe formulated as:

|T i(P),Q|<ε T

ε TBe the error control factor.Attention is every couple of (P in cluster process i, Q * j) only by cluster once, i.e. (P i, Q * j) belong to and can only belong to a class.Cluster process must to all not cluster cross other maybe match points and related respective neighbours point verify.The beginning condition of cluster is also to exist among the goodClass not have cluster to cross (P next time i, Q * j).

(3) cluster result is analyzed,, must it be deleted because generally there is erroneous matching among the structure matching result.In cluster result, the class that the definition element is maximum is a maximum kind, and the class of element more than second is inferior big type, a pair of match point of each element representation in the class.Generally, the unique point of correct match related local map also be correct, all correct match points all can gather in the error allowed band into one type, the point of respective class is very many to meeting; Otherwise, the point of erroneous matching related local map randomness very big, element is considerably less in corresponding type.Therefore the present invention is optimized coupling as follows: if the ratio of maximum kind and the element number of inferior big class is greater than certain threshold value; Perhaps the maximum kind element number is greater than inferior big type; The maximum kind element number is greater than certain threshold value simultaneously, and the present invention will export maximum kind as corresponding relation, carries out optimal transformation and estimates; Otherwise the present invention thinks that then two point set correlativitys of coupling are too poor, can't mate.

Description of drawings

Fig. 1: process flow diagram of the present invention.

Fig. 2: unique point P cKNN space structure figure and corresponding ring type corner sign indicating number.

Fig. 3: similar ring type corner sign indicating number and corresponding similar space structure.

Fig. 4: similarity is calculated synoptic diagram.

Fig. 5: two images to be matched.

Fig. 6: the matching result of two width of cloth images shown in Figure 5.

Embodiment

Below provide instantiation to further specify application of the present invention.

Embodiment 1

Match objects is seen two width of cloth images shown in the accompanying drawing 5.Two width of cloth image takings are in different time, and different angles have only partial content identical, and the image size is 440 * 330.

Moving equipment of the present invention is Gateway T6307c notebook, Intel 2core 1.6G, 1G internal memory.The present invention has platform transplantation property, and concrete implementation platform is GCC environment and the Visual C++2005 environment under the Windows XP/Server 2003 under the Centos.Operational efficiency GCC is better than Visual C++2005.

The present invention stipulates the Euclidean distance error ε between equal error ε=0.1, two pixel of two scalars T=10, define two kinds of errors and be intended to calculate similar ring type corner sign indicating number and cluster.Neighbours' number of the unique point of the present invention simultaneously is set to K=15.

The practical implementation situation is following:

Step 1: from two images to be matched, obtain two feature point sets as input, unique point only need comprise that two-dimensional coordinate information gets final product.The present invention has adopted single scale Harris Corner Detection Algorithm, Harris response coefficient α=0.06, difference yardstick σ D=3, level and smooth yardstick σ I=7, it is 3 * 3 that local non-maximum value suppresses window, and inhibiting value is 0.01 times of overall maximum angle point response.Two width of cloth images extract 189 and 208 unique points respectively.Corresponding point set is designated as S and T, n respectively 1=189 and n 2=208.

Step 2: similarity is calculated, and 15 neighbours that extract each unique point are according to the ring type corner sign indicating number of the definition calculated characteristics point of ring type corner sign indicating number.In computation process, avoid removing Z-operation.When scalar is done division, as required, add or deduct a very little positive number, in order to avoid program crashing.

Based on the definition of similar ring type corner sign indicating number, calculate the similarity of two characteristic points to be matched, based on classifying rules, confirm the element among goodClass and the unknownClass, calculate simultaneously element among the goodClass related local map.Concrete steps are following:

(1) sets up the search volume.208 * 15 corner sign indicating number (E to the ring type corner sign indicating number of 208 unique points among the T n, θ n) sort to set up index according to angular dimension.

(2) confirm object search.For obtaining P iThe point Q that possibly mate * j, need be to P iRing type corner sign indicating number on 15 corner sign indicating number (E k, θ k) (k=0,1 ..., 15) search for respectively.

(3) confirm search strategy.Select each (E successively k, θ k), search for by angle in the search volume, if search for successfully, the corner sign indicating number (E that is then searching n, θ n) front and back continuation linear search other corners sign indicating number (E that angle equates in error range n, θ n), if (E k, θ k) and (E n, θ n) comparative result do not need add operation, then respectively with (E k, θ k) and (E n, θ n) be the ring type corner sign indicating number of two equivalences of dot generation and utilize maximal phase that the increment matching algorithm calculates them like ring type corner sign indicating number.

(4) the definite unique point that possibly mate, P i15 (E k, θ k) after all search finishes, keep the maximum similar ring type corner sign indicating number of similar length in the computation process, with the maximal phase that keeps like related another unique point Q of ring type corner sign indicating number jAs P iThe point Q that possibly mate * j, then all preserve if any a plurality of.

Step 3: structure matching, 189 unique points among the S are classified according to similarity and the unique point number that possibly mate.Keep goodClass and unknownClass.And according to the local map of elements correlation among the similarity transformation Model Calculation goodClass.The similarity transformation model is following:

X=sxcosθ-sysinθ+t x

Y=sxsinθ+sycosθ+t y

S wherein, θ is respectively the zoom factor and the anglec of rotation, t x, t yIt is respectively the translational movement of x and y direction.(x is y) with (X Y) is the unique point coordinate of conversion front and back respectively.Note in the local similar transformation calculations process, just the possible match point one group respective neighbours point related with them can be estimated one group of conversion coefficient, if so P i, Q * jSimilarity is ls, then can estimate the conversion of ls group local similar, and the present invention organizes ls the average of local similar conversion as the final local similar conversion of estimating.

Step 4: optimization of matching, sort by similar length to the element among the goodClass in the step 3 from big to small.Because similar length is big more, the local space structure of two unique points of expression is similar more, and the possibility of coupling is also big more, from the maximum P of similar length i, Q * jThe beginning cluster just can guarantee that also the cluster effect is good more, and the matching effect that obtains is also good more.So the maximum a pair of unique point of similarity that cluster is at first selected not gather class begins.Carry out cluster by clustering rule then.To the splicing and the recognition application of large scale scene image, establish s 1, s 2Be maximum kind and time element number of big class, if s 1>=2s 2Perhaps s 1>s 2, s 1>23, algorithm will be used for optimal transformation and estimate the output of the element in the maximum kind as the optimization of matching result.Otherwise algorithm will think that coupling is unsuccessful.To the instance of accompanying drawing 5, cluster result one has 88 types, and wherein the maximum kind number of elements is 33, and inferior big dvielement number is 6.Through observing, the maximum kind element all is correct match in error range, and inferior big dvielement all is erroneous matching in error range.In accompanying drawing 5 example illustrated, maximum kind is 5.5 with inferior big dvielement number ratio, satisfies condition, and the match is successful.The element of maximum kind will carry out optimal transformation and estimate as corresponding relation.

Step 5: conversion estimates that because generally, the conversion between the scene image often has affine or view transformation to a certain degree, so the present invention adopts the affined transformation model to estimate the conversion between the scene image.The affined transformation model is following:

X=ax+by+t x

Y=cx+dy+t y

A wherein, b, c, d are respectively rotations, convergent-divergent and stretching factor, t x, t yIt is respectively the translational movement of x and y direction.(x is y) with (X Y) is the unique point coordinate of conversion front and back respectively.To all match points,, estimate to obtain optimal transformation thereby carry out least square fitting by the linear system of equations of affined transformation model group.To the instance of accompanying drawing 5, optimal transformation does 1.04237 0.00366 - 333.03 0.01088 1.02362 - 10.1731 , Optimal transformation is applied to matching image, obtains matching result, see accompanying drawing 6.The result shows that the present invention has only the identical image of partial content to be stitched together well two width of cloth in error range.

Claims (2)

1. the method for registering images based on unique point is characterized in that, may further comprise the steps:
Step 1): feature extraction,
The imagery exploitation feature point extraction operator of two width of cloth being treated registration carries out feature point extraction, and two feature point sets that obtain are defined as the input data of method;
Step 2): similarity is calculated,
Set up the search volume, defining said two feature point sets is S and T, and the number of the point that is comprised is defined as n respectively 1And n 2, the ring type corner sign indicating number of the k nearest neighbor structure of extract minutiae and construction feature point; The search volume is defined as the pairing n of point set T 2N on the sign indicating number of individual ring type corner 2K corner sign indicating number is to n 2K corner sign indicating number set up index by the angular dimension ordering; The k nearest neighbor structure of unique point is through a ring type corner representation; Utilize maximal phase between binary chop and the increment matching algorithm calculated characteristics point like ring type corner sign indicating number; Maximal phase is defined as two similarities between the unique point like the similar length of ring type corner sign indicating number, confirms when calculating similarity that the unique point that possibly mate is right;
Described ring type corner sign indicating number is: with unique point respectively with its k nearest neighbor structure in K neighbours o'clock be connected through a limit; With the combination at the angle of every limit and its counterclockwise adjacency be defined as a corner sign indicating number EAC=(E θ), wherein has K corner sign indicating number (Ei, θ i), i=0,1 ..., K-1; This K corner sign indicating number also is linked to be the ring type corner sign indicating number that a loop configuration then obtains unique point with them successively by counterclockwise sequential encoding, and definition K is the length of ring type corner sign indicating number; It is proportional that two ring type corner sign indicating numbers begin to satisfy corresponding sides from a certain opposite side, and corresponding angles are proportional and equal 1, and it is similar then to define these two ring type corner sign indicating numbers, and the length of one of them ring type corner sign indicating number is defined as the similar length of similar ring type corner sign indicating number;
Step 3): structure matching,
Count with mating according to the similarity size, unique point to classifying and carrying out local map and estimate, is made (P i, Q * j) representation feature point is right, unique point is defined as class condition:
( P i , Q * j ) &Element; badClass if similarity ( P i , Q * j ) < 2 unknownClass if similarity ( P i , Q * j ) &GreaterEqual; 3 and NO > 1 goodClass if similarity ( P i , Q * j ) &GreaterEqual; 3 and NO = 1
(P among the goodClass i, Q * j) be defined as the structure matching result, to (the P among the goodClass i, Q * j) adopt the similarity transformation model to carry out the local map estimation, definition (P i, Q * j) similarity be ls, (P i, Q * j) put combination with the every couple of neighbour and estimate the conversion of ls group local similar altogether, the average of definition ls group local similar conversion is as the final local map of estimating; Q wherein * jBe P iPossible match point; Described NO is Q j *Number; Similarity (P i, Q * j) implication is Q * jWith P iSimilarity;
Step 4): optimization of matching,
Utilize the structure matching result to carry out cluster, confirm the unique point corresponding relation through following step:
(a) to (P among the goodClass i, Q * j) by the descending ordering of similarity;
(b) to (P after the ordering i, Q * j) carry out descending scanning by the similarity size, as (P i, Q * j) not by the cluster mistake, then with (P i, Q * j) type of being center C i, (P i, Q * j) related local map T iAs sorter, among goodClass and the unknownClass by the unique point of cluster to and related respective neighbours point do not verify;
At first will with (P i, Q * j) related ls directly is included into such to neighbours' point; It is right to utilize among other goodClass of sorter checking and the unknownClass not possible the match point of cluster that the neighbours that reach related coupling are put then, satisfies sorter T in the error range when the point of being verified ties up to the volume coordinate pass iThe time, then it is carried out mark, it is included into such, do not carry out subsequent treatment, otherwise it is ignored;
(c) analyze cluster result,
In the cluster result, the class definition that element is maximum is a maximum kind, and the class of element more than second is a time big class; The maximum kind element number is defined as S 1, inferior big dvielement number is defined as S 2, when satisfying one of following condition, maximum kind is estimated optimal transformation as the unique point corresponding relation,
1. S 1>t 1S 2, 2. S 1>S 2While S 1>t 2,
T wherein 1, t 2Be specific threshold, by the kind decision of image to be matched greater than 1;
Step 5): the optimal transformation estimation,
Utilize the affined transformation model that least-squares estimation is carried out in the mapping between two width of cloth images.
2. the method for registering images based on unique point according to claim 1 is characterized in that, described similarity is calculated through following step and accomplished:
(1) set up the search volume, defining said two feature point sets is S and T, and the number of the point that is comprised is defined as n respectively 1And n 2, define P simultaneously i∈ S, Q j∈ T, the k nearest neighbor of extract minutiae is also pressed the ring type corner sign indicating number that defines construction feature point; The search volume is defined as the pairing n of point set T 2N on the sign indicating number of individual ring type corner 2K corner sign indicating number is to n 2K corner sign indicating number set up index by the angular dimension ordering;
(2) confirm object search, the single object search is defined as P iRing type corner sign indicating number on each corner sign indicating number, object search is K;
(3) confirm search strategy, definition (E k, θ k) be P iA corner sign indicating number, utilize binary search and linear search all identical corner sign indicating number (E of angle in the seek error allowed band in the search volume n, θ n), definition (E n, θ n) unique point that is subordinate to is Q j
(4) utilize the increment matching algorithm to calculate similar ring type corner sign indicating number, with (E k, θ k) and each (E n, θ n) compare, as (E k, θ k) and (E n, θ n) compare operation be true time, respectively with (E k, θ k) and (E n, θ n) be dot generation P iAnd Q jTwo ring type corner sign indicating number L P={ A 0, A 1..., A (K-1)And L Q={ B 0, B 1..., B (K-1), A wherein k∈ L pAnd B n∈ L QExpression corner sign indicating number, note E k A, θ k A, E n B, θ n BRepresent A respectively k, B nBian Hejiao;
At first compare A 0, B 0If, θ 0 A, θ 0 BUnequal, the next corner sign indicating number that the corner sign indicating number that then angle is little is adjacent carries out add operation and generates new corner sign indicating number; If θ 0 A, θ 0 BEquate, then verify θ 0 A, θ 0 BAdjacent corresponding sides (E 0 A, E 0 B) and (E 1 A, E 1 B) whether proportional, if proportional, then with A 0, B 0Be retained in the similar ring type corner sign indicating number of having tried to achieve, if disproportionate, then with this A 0, B 0Carry out add operation with adjacent separately next corner sign indicating number and generate new corner sign indicating number, next compare operation is carried out in circulation then, and cyclic process is till one of them ring type corner sign indicating number has traveled through;
(5) confirm the possibility match point, definition S (A k, B n)={ L k, L nRepresent with E k A, E n BBe the similar ring type corner sign indicating number of initial corresponding sides, L k, L nRepresent similar ring type corner sign indicating number, make S (A k, B n) similar length be len Ij(k, n), unique point P i, Q jSimilarity be defined as:
similarity ( P i , Q j ) = max k , n &Element; [ 0 , K - 1 ] len ij ( k , n )
Work as Q j *Satisfy Q j * = Arg Max Q j &Element; T Similarity ( P i , Q j ) The time, definition Q j *Be P iPossible match point, Q j *Number be defined as NO.
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