ex_7

聚类算法 k均值 该算法简单 容易看懂 是基本的算法(kmeans clustering )

说明：  自己编写的zero forcing equalizer，完全可用(I have written zero forcing equalizer, fully available)

Several methods for solving equations matlab

利用matlab求响应程序，可以求出系统的响应 (use this project can sovle the problem )

图像压缩的MATLAB程序，可用于压缩任意图像(Image compression MATLAB program)

本程序主要是应用模糊推理进行分类，实验后处理效果明显(This procedure is mainly classified fuzzy reasoning)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )

特征空间波束形成图 稳健 改进算法 比较了lcmv特征空间 改进的特征空间 (The feature space beamforming Figure steady improvement algorithm)

说明：  从图像经灰度级压缩，计算灰度共生矩阵，提取纹理特征参数，包括有能量，熵，惯性矩，对比度，相关度。算法包含有matlab封装函数计算方式，以及自行变成计算方式，两种纹理特征提取算法。(Gray level compression is used to calculate gray level co-occurrence matrix and extract texture feature parameters, including energy, entropy, moment of inertia, contrast and correlation. The algorithm includes two kinds of texture feature extraction algorithms: matlab encapsulation function calculation method and self-transformation calculation method.)

this code implement a fingerprint recognition method