近邻传播算法 matlab 代码,用Afinity Propagation(AP)近邻传播算法如何提取曲面边界...
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用Afinity Propagation(AP)近邻传播算法如何提取曲面边界
我现在用K-means算法已经求得了聚类的中心点，之前我是用V图的方法实现的，现在想用AP聚类算法提取出曲面的边界，我只是把点提取出来了没有出现边界，要如何实现呢？如果可以用其他的算法实现也可以。请各位赐教，不胜感激！！！(以下是我前部分的程序)
clear;clc;
syms u v;
x=-40+35*v+5*v*v;
y=-58*u+18*u*u;
z=6*(u*u*v*v+u*u*v)-4*u*v*v+34*u*u+4*v*v-14*u*v-50*u+25*v+6;
dxdu=diff(x,u,1);
dxdv=diff(x,v,1);
dydu=diff(y,u,1);
dydv=diff(y,v,1);
dzdu=diff(z,u,1);
dzdv=diff(z,v,1);
d2xdu2=diff(x,u,2);
d2xdv2=diff(x,v,2);
d2ydu2=diff(y,u,2);
d2ydv2=diff(y,v,2);
d2zdu2=diff(z,u,2);
d2zdv2=diff(z,v,2);
dsdu=dxdu*dxdu+dydu*dydu+dzdu*dzdu;
dsdv=dxdv*dxdv+dydv*dydv+dzdv*dzdv;
d2sdu2=diff(dsdu,u,1);
d2sdv2=diff(dsdv,v,1);
d2sdudv=diff(dsdu,v,1);
E=dsdu*dsdu;
F=dsdu*dsdv;
G=dsdv*dsdv;
L=d2sdu2;
M=d2sdudv;
N=d2sdv2;
E=inline(vectorize(E));
F=inline(vectorize(F));
G=inline(vectorize(G));
L=inline(vectorize(L));
M=inline(vectorize(M));
N=inline(vectorize(N));
pointer_A=0;
pointer_B=0;
pointer_C=0;
pointer_X=0;
pointer_Y=0;
pointer_Z=0;
for a=0:0.125:1
for b=0:0.125:1
E_cal=E(a,b);
F_cal=F(a,b);
G_cal=G(a,b);
L_cal=L(a,b);
M_cal=M(a,b);
N_cal=N(a,b);
K=(L_cal.*N_cal-M_cal.^2)./(E_cal.*G_cal-F_cal.^2);
H=(E_cal.*N_cal-2.*F_cal.*M_cal+G_cal.*L_cal)./(E_cal.*G_cal-F_cal.^2)/2;
if K>0&H>0;
pointer_A=pointer_A+1;
A(pointer_A, 1)=a;
A(pointer_A, 2)=b;
elseif K>0&H<0;
pointer_B=pointer_B+1;
B(pointer_B, 1)=a;
B(pointer_B, 2)=b;
elseif K==0&H>0;
pointer_C=pointer_C+1;
C(pointer_C, 1)=a;
C(pointer_C, 2)=b;
elseif K==0&H==0 ;
pointer_X=pointer_X+1;
X(pointer_X, 1)=a;
X(pointer_X, 2)=b;
elseif K==0&H<0 ;
pointer_Y=pointer_Y+1;
Y(pointer_Y, 1)=a;
Y(pointer_Y, 2)=b;
elseif K<0 ;
pointer_Z=pointer_Z+1;
Z(pointer_Z, 1)=a;
Z(pointer_Z, 2)=b;
end
end
end
if pointer_A>0;
opts = statset('Display','final');
[Idx,Ctrs_A,SumD,D] = kmeans(A,3,'Replicates',3,'Options',opts);
Ctrs_A ;
else
Ctrs_A=[];
end
if pointer_B>0;
opts = statset('Display','final');
[Idx,Ctrs_B,SumD,D] = kmeans(B,3,'Replicates',3,'Options',opts);
Ctrs_B ;
else
Ctrs_B=[];
end
if pointer_C>0;
opts = statset('Display','final');
[Idx,Ctrs_C,SumD,D] = kmeans(C,3,'Replicates',3,'Options',opts);
Ctrs_C ;
else
Ctrs_C=[];
end
if pointer_X>0 ;
opts = statset('Display','final');
[Idx,Ctrs_X,SumD,D] = kmeans(X,3,'Replicates',3,'Options',opts);
Ctrs_X ;
else
Ctrs_X=[];
end
if pointer_Y>0 ;
opts = statset('Display','final');
[Idx,Ctrs_Y,SumD,D] = kmeans(Y,3,'Replicates',3,'Options',opts);
Ctrs_Y;
else
Ctrs_Y=[];
end
if pointer_Z>0;
opts = statset('Display','final');
[Idx,Ctrs_Z,SumD,D] = kmeans(Z,3,'Replicates',3,'Options',opts);
Ctrs_Z ;
else
Ctrs_Z=[];
end
SumD ;
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