手写数字识别代码.docx

function feature = scFeature(im) %把三层的RGB图像转化成二层的灰度图 if length(size(im) = 3 I = im(:,:,1); else I = im; end row, col = size(I); bwI = im2bw(I); %样本图像二值化 bwI = bwI; count = 1; %提取样本特征 for i = 1: 5 for j = 1:5 imTemp = bwI(row/5*(i-1) + 1:row/5*i,col/5*(j-1) + 1:col/5*j);%将数字区域平均分为5*5的小区域 numTemp = sum(sum(imTemp); %每个区域像素总数 feature(count) = 25*numTemp/(row*col); %每个小区域黑像素所占比例 count = count + 1; end end %函数功能建立样本库 clc clear for i= 1:10for j = 1:25 I = imread(strcat(C:Documents and SettingsAdministrator桌面 图片,num2str(i-1),s,mynum2str(i), (,num2str(j),),.bmp); I = I(:,:,1); %对读入的图像进行二值化 bwI = im2bw(I); %裁剪读入图像 r,c = find(bwI=0); rmin = min(r); rmax = max(r); cmin = min(c); cmax = max(c); II = I(rmin:rmax,cmin:cmax); %把裁剪图像放大成规格的150*150 III = imresize(II,150 150); %生成样本特征 feature(:,j,i) = scFeature(III); end end %以结构体形式存储样本特征 for i = 1:10 pattern(i).num = 25; pattern(i).feature = feature(:,:,i); end save myTemplet %函数功能降维 function y1,y2=pcapro(sample) load myTemplet pattern;%加载样品库 mixedsig=; sum1=0; %将所有类别的所有样品合并到mixedsigfor i=1:10; sum1=sum1+ pattern(i).num; mixedsig=mixedsig pattern(i).feature; end Dim,NumofSampl=size(mixedsig);%Dim为特征值NumofSampl为样品总个数 dsig_cov=cov(mixedsig);%求协方差矩阵 %利用pcacov()函数求的从小到大的排序好的协方差矩阵的特征值latent和相应特征向量pc pc,latent,tspuare=pcacov(dsig_cov); temp=0;con=0;m=0; %根据贡献率取舍特征向量 sum2=sum(latent); for i=1:25 if(cony0 classfit=class1; else classfit=class2; end % %函数名称fisher() %参数sample待识别样品特征 %返回值y待识别样品所属类别 %函数功能Fisher分类器 % function y=fisher(sample); clc; num=zeros(1,10); classnum=0; for i=1:10 for j=1:i classnum=fisherclassify(i,j,sample); num(classnum)=num(classnum)+1; end end max_val,max_pos=max(num); y=max_pos-1; % %函数名称caijian()%函数功能对图像进行裁剪然后进行特征提取 %参数读入图像I %返回值特征y % function y=caijian(I) I = I(:,:,1); %对读入的图像进行二值化 bwI = im2bw(I); %裁剪读入图像 r,c = find(bwI=0); rmin = min(r); rmax = max(r); cmin = min(c); cmax = max(c); II = I(rmin:rma