Embodiment:
One, improved SURF and SIFT algorithm
1. from two width of cloth images, detect and coupling with SURF or SIFT algorithm
2. two stack features point, the unique point of coupling sorts from high to low to the similarity degree according to them.
3. detect the profile information of two width of cloth images or the information on limit.
To the unique point of a pair of coupling to and a near opposite side calculate its corresponding TAR figure.The foundation of the calculating of TAR figure is the affine constant TAR of being, it calculates according to leg-of-mutton three apex coordinates.If an Atria summit is respectively: p
B(x
b, y
b), p
M(x
m, y
m), p
E(x
e, y
e), we have so
To the unique point pR among Fig. 1 (a) and limit p
r i(i=0,1,2 ... n).p
r i(i=0,1,2 ... n) limit E
rOn point set.Fig. 2 (a) is the TAR figure ImR of their correspondences.To the unique point pT among Fig. 1 (b) and limit E
tp
t i(i=0,1,2 ... m) limit E
tOn point set.Same Fig. 2 (b) is point and TAR figure ImT corresponding to limit among Fig. 1 (b).Their mutually element value computing formula is:
5. TAR figure ImR and ImT are used SURF or SIFT algorithm, and their Feature Descriptor is improved mutually element value (TAR value information) and the isocontour information of having added corresponding point among the TAR figure.Thereby find stack features point, and the descriptor after the application enhancements is found out the corresponding relation between them.The unique point correspondence of a pair of mutual coupling like this one diabolo, such as: shown in Fig. 1 (c) and Fig. 1 (d).
6. calculated near the affined transformation of two parts the unique point by the triangle pair that mutually finds coupling.
7. extracting equably one out from the limit of image simultaneously marks words and phrases for special attention to CP
rAnd CP
s, and allow them take on the role of a part of unique point descriptor.To a pair of unique point to p
rAnd p
s, find out three pairs of points from overlapped border to (pr according to the local affine transformations of finding out in the 5th step
1, ps
1), (pr
2, ps
2), and (pr
3, ps
3) any 3 conllinear not among the .l.Ps
1 i, ps
2 j, ps
3 kTo meet the following conditions and the point set on the border:
Quadrilateral pr then, pr
1, pr
2, pr
3With one group of quadrilateral ps, ps
1 i, ps
2 j, ps
3 kDetermine one group of geometric transformation F.Wherein l is in borderline hunting zone.Then this part similar value is:
Then improved similar value is:
SM(p
r,p
t)=SMD(p
r,p
t)*||p
t,f(p
r)||-α*SME(p
r,p
t)
8. according to the geometric transformation parameter of having estimated and improved Feature Descriptor, recomputate the coupling between unique point.And then calculate overall geometric transformation parameter.
9. on this basis, adopt similarity measure function in this paper, adopt alternative manner, realize the accuracy registration of image.
The advantage that SURF after the improvement or SIFT algorithm possess:
Such as Fig. 8 and shown in Figure 9.The unique point that algorithm after the improvement improves significantly to correct matching rate.
Two, the new similarity measure function based on Li Sa such as figure that proposes in this method:
1. Lee's Sa is such as figure
In the mathematics category, Li Sa such as figure are the movement locus that has following parameter system of equations to determine
2. be used for to calculate the selection of the point set on the track of similarity.At first, given parameters
A
x, A
y, ω
x, ω
y, φ
x, φ
y, for the some pS among the pR in figure R and the figure S, the Trajectories Toggle of generation is gR
1, choose equally spacedly one group of point set according to parameter t and be designated as pR
1 i(i=1,2,3 ... n).By track gR
1The track with respect to a pR that produces is designated as gR
2, the point set on it is designated as pR
2 i(i=1,2,3 ... n).Satisfy following relational expression between them.
PR
2 i(i=1,2,3 ... n) be with respect to a pR and the point set on the selected track that is used for calculating similarity measure function, wherein α is given constant.
If the geometric transformation between figure R and figure S is described with f, the track point set selection course with respect to pS in figure S is as follows: to point set pR+pR
1 i(i=1,2,3 ... n) carry out the f conversion and obtain point set pS
1 i(i=1,2,3 ... n), by point set pS
1 i(i=1,2,3 ... n) and the point set pS that determines of some pS
2 i(i=1,2,3 ... n).Satisfy following relational expression between them
PS
2 i(i=1,2,3 ... n) be with respect to a pS and the point set on the selected track that is used for calculating similarity measure function.
3. suppose: S
lBe labeled as a kind of similarity measure function, based on the point set of above definition, the similarity measure that we propose is: α.
PR wherein
2Be selected point set pR
2 i(i=1,2,3 ... n), pS
2Be selected point set pS
2 i(i=1,2,3 ... n).
The advantage of this similarity measure function:
(a) relative other similarity measure function, this similarity measure function can in the situation that other similarity measure function lost efficacy, still effective, wherein sea area as shown in Figure 5.
(b) possesses the error amplification.
(c) can calculate according to different tracks a plurality of similarity measures, thus more stable.
(d) by the track disturbing phenomenon, this similarity measure has higher alignment accuracy.