一只在cv道路上爬行的小乌龟~

1.模糊关系function [rm] = step1_mission(samples) %%%%%%%%%% Begin %%%%%%%%%% [m,n] = size(samples); formalMt = zeros(m,n); rm = zeros(m,m); % 矩阵进行正规化 minele = min(min(samples)); maxele = max(max(samples)); for mm =1:m

1.聚类分析基础function [ distance ] = step1_mission( v1, v2 ) %%%%%%%%%% Begin %%%%%%%%%% [m,p]=size(v1); [n,p]=size(v2); distance=zeros(m,n); for mm=1:m for nn=1:n distance(mm,nn)=sqrt(sum((v1(mm,:)-v2(nn,:)).^2));

1.计算等价集和下近似集load ('template.mat');patternNum=75;%每个类别取75个样品bottom=zeros(patternNum*2,1);bottom(1:patternNum,1)=0;bottom(patternNum+1:patternNum*2,1)=1;x=[pattern(1).feature(1:patternNum,:); pattern(2).feature(1:patternNum,:)];%取75个第一类样品和75个第二类样品按列连

基于感知器算法的手写数字识别% --------------------------------------------------------------------% 感知器算法% --------------------------------------------------------------------load('template.mat');img=imread('untitled.bmp'); %读入测试图像feature=Getfeature(img); %提取测试

1.计算类内离散度矩阵% --------------------------------------------------------------------% 类内离散度矩阵（计算第一类“0”和第二类“1”的类内离散度矩阵）% --------------------------------------------------------------------load('template.mat');xmeans0 = zeros(1,25); %第一类类的均值xmeans1 = z

1.计算每一类手写数字样品的均值% -----------------------------% 计算每一类手写数字样品的均值% -----------------------------load('template.mat');xmeans=zeros(1,25);%% 计算每一类手写数字样品的均值 for i = 1:10 %%%%%%%%%% Begin %%%%%%%%%%\xmeans=mean(pattern(i).feature); %%%%