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显著性区域检测matlab_code_20170412

1.Main_program吕品晶

%====test1

clear

close all

clc

燃气的互换性%img_in,输入的待处理的图像足摩

%n，图像分块的patch的大小

电子束加工%img_rgb:缩放后的rgb图

%img_lab：rgb2lab后的图

%h，w，图像高宽

%mex_store,存储矩阵

img=imread('test1.jpg');

% img=imread('E:\例程\matlab\显著性分割\假设已知均值\test picture\0010.tif');

max_side=120;神州天戈

yu_value=0.8;

img_lab = pre_rgb2lab(img,max_side);

[ img_scale_1,img_scale_2,img_scale_3,img_scale_4 ] = get4Scale( img_lab );

[ DistanceValue_scale_1_t1,DistanceValue_scale_1_exp,DistanceValue_scale_1_t1_rang,DistanceValue_scale_1_exp_rang] = distanceValueMap_search_onescale_2( img_scale_1,max_side );

[ DistanceValue_scale_2_t1,DistanceValue_scale_2_exp,DistanceValue_scale_2_t1_rang,DistanceValue_scale_2_exp_rang] = distanceValueMap_search_onescale_2( img_scale_2,max_side );

[ DistanceValue_scale_3_t1,DistanceValue_scale_3_exp,DistanceValue_scale_3_t1_rang,DistanceValue_scale_3_exp_rang] = distanceValueMap_search_onescale_2( img_scale_3,max_side );

[ DistanceValue_scale_4_t1,DistanceValue_scale_4_exp,DistanceValue_scale_4_t1_rang,DistanceValue_scale_4_exp_rang] = distanceValueMap_search_onescale_2( img_scale_4,max_side );

value_C_1=Center_weight( DistanceValue_scale_1_exp_rang,yu_value );

value_C_2=Center_weight( DistanceValue_scale_2_exp_rang,yu_value );

value_C_3=Center_weight( DistanceValue_scale_3_exp_rang,yu_value );

value_C_4=Center_weight( DistanceValue_scale_4_exp_rang,yu_value );

[h,w]=size(value_C_1);

value_C_1_resize=value_C_1;

value_C_2_resize=imresize(value_C_2,[h,w]);

value_C_3_resize=imresize(value_C_3,[h,w]);

value_C_4_resize=imresize(value_C_4,[h,w]);

value_C_sum=(value_C_1_resize+value_C_2_resize+value_C_3_resize+value_C_4_resize)/4;

figure,

imshow(value_C_sum);

figure,

imshow(value_C_1_resize);

figure,

imshow(value_C_2_resize);

figure,

imshow(value_C_3_resize);

figure,

imshow(value_C_4_resize);

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2.Center_weight

function [ img_out ] = Center_weight( img_in,yu_value )

%UNTITLED Summary of this function goes here

% Detailed explanation goes here

img_in=mat2gray(img_in);

[h,w]=size(img_in);

[row,col]=find(img_in>yu_value);%到所有大于阈值的点，记录坐标

l=length(row);

a(1,:)=row;

a(2,:)=col;

for i0=1:h

for j0=1:w

for i=1:l

c_store(i)=norm(a(:,i)-[i0;j0]);

end

min_c(i0,j0)=min(c_store);

end

min_c_rang=mat2gray(min_c);

img_out=img_in.*(1-min_c_rang);

end

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3.distanceValueMap_search_onescale

鼓词function [ DistanceValue_t1,DistanceValue_exp,DistanceValue_t1_rang,DistanceValue_exp_rang] = distanceValueMap_search_onescale( img_in,max_side )

%UNTITLED7 Summary of this function goes here

% Detailed explanation goes here

[h,w]=size(img_in(:,:,1));

img_scale1=img_in;

%================================================================================

%在计算块之前，考虑边缘点，所以先对矩阵进行填充

q=3;%填充边缘像素个数

img_scale1_pad(:,:,1)=padarray(img_scale1(:,:,1),[q,q],'replicate','both');

img_scale1_pad(:,:,2)=padarray(img_scale1(:,:,2),[q,q],'replicate','both');

img_scale1_pad(:,:,3)=padarray(img_scale1(:,:,3),[q,q],'replicate','both');

%----------------------------------------------------------------------------------

%计算该尺度下存储patch相量的矩阵，各三个像素取一个patch

for i=1:h

for j=1:w

img_scale1_patchVstore_all(i,j,:)=reshape(img_scale1_pad(i:i+6,j:j+6,:),1,147);

end

img_scale1_patchVstore_x=img_scale1_patchVstore_all(1:q:h,1:q:w,:);%将代表patch的相量存为一个矩阵

disp('img_scale1_patchVstore_x complete');

%-----------------------------------------------------------------------------------------

%开始计算每一点的距离的值

[h_x,w_x]=size(img_scale1_patchVstore_x(:,:,1));%记录存储矩阵的大小

for i0=1:h

disp(num2str(i0));

for j0=1:w

temp1=img_scale1_patchVstore_all(i0,j0,:);

store_tp1_all=reshape(img_scale1_patchVstore_all(i0,j0,:),1,147);

for i=1:h_x

for j=1:w_x

store_tp1_x=reshape(img_scale1_patchVstore_x(i,j,:),1,147);

store_o_disvalue_scale1_x(i,j)=norm( store_tp1_all-store_tp1_x);

%将与mex_store_tp1像素和其他全图像素计算的欧式距离存储

store_position_dis_x(i,j)=norm([i0,j0]-[(1+(i-1)*3),(1+(j-1)*3)]);

end

%---将距离值进行归一化--------------------------------

max_1=max(max(store_o_disvalue_scale1_x));

本文发布于:2024-09-23 23:31:31，感谢您对本站的认可！

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