kde.rar

clear; %清除之前的变量 clc; train_path = 'F:\kde\train_set2\';%训练图片的路径 for i = 1:20 train_set{i} = imread([train_path,num2str(i), '.jpg']); end %连续读入20张训练样本 test_set = imread('F:\kde\test_set2\test.jpg');%测试图片的路径 [Height, Width, Channel] = size(test_set);%计算图片的高度，宽度和通道数 h = 30; %带宽设为20 for m = 1:Height for n = 1:Width for i = 1:20 if ( ((double(test_set(m, n, 1)) - double( train_set{i}(m, n, 1))) / h).^2 <= 1) ... && (((double(test_set(m, n, 2)) - double( train_set{i}(m, n, 2))) / h).^2 <= 1) ... &&(((double(test_set(m, n, 3)) - double( train_set{i}(m, n, 3))) / h).^2 <= 1) k(i) = (1 - ((double(test_set(m, n, 1)) - double(train_set{i}(m, n, 1))) / h).^2) ... * (1-((double(test_set(m,n,2))-double(train_set{i}(m,n,2)))/h).^2) ... *(1-((double(test_set(m,n,3))-double(train_set{i}(m,n,3)))/h).^2); else k(i)=0; end end Pr(m, n) = 15 / (8 * pi * 20 * (h^3)) * sum(k);%计算基于Epanechnikov核函数的三维通道彩色图像像素值的概率密度估计函数 end end colormap(jet); mesh(Pr); view(40,-90); thresh = graythresh(Pr); I = 1-im2bw(Pr,thresh); imshow(I)