【图像提取】PCA-CSIFT feature图像特征提取【含Matlab源码 1174期】_基于matlab pca-csift feature图像特征提取

⛄一、获取代码方式

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⛄二、简介

⛄三、部分源代码

%%initial image and create input H image.
clear;
clc
img=imread(‘test.png’);
row=256;
colum=256;
img=imresize(img,[row,colum]);
transfer_matrix=[0.06,0.63,0.27;0.3,0.04,-0.35;0.34,-0.6,0.17];
R=img(:,:,1);
G=img(:,:,2);
B=img(:,:,3);
e=transfer_matrix(1,1)R+transfer_matrix(1,2)G+transfer_matrix(1,3)B;
e1=transfer_matrix(2,1)R+transfer_matrix(2,2)G+transfer_matrix(2,3)B;
e2=transfer_matrix(3,1)R+transfer_matrix(3,2)G+transfer_matrix(3,3)B;
H=(double(e1)./double(e2));
origin=img;
img=H;
[m,n]=size(img);
%% Scale-Space Extrema Detection
% original sigma and the number of actave can be modified.
sigma=1.6;
octave=3;%6sigmak^{(octave*level)<=min(m,n)/(2}(octave-2))
level=5;
k=2^(1/level);
D=cell(1,octave);
for i=1:octave
D(i)=mat2cell(zeros(row2^{(2-i)+2,colum*2}(2-i)+2,level),row2^{(2-i)+2,colum*2}(2-i)+2,level);
end
% first image in first octave is created by interpolating the original one.
temp_img=img(1:1/2:m,1:1/2:n);
temp_img=padarray(temp_img,[1,1],‘pre’);
temp_img=padarray(temp_img,[1,1],‘replicate’);
%create the DoG pyramid.
for i=1:octave
temp_D=D{i};
sigma=sigmasqrt(2)^(i-1);
for j=1:level
p=(level)(i-1);
figure(1);
subplot(octave,level,p+j);
f=fspecial(‘gaussian’,[1,floor(6sigmak^{(p+j-1))],sigma*k}(p+j-1));
L1=temp_img;
if(i1&&j1)
L2=conv2(temp_img,f,‘same’);
L2=conv2(L2,f’,‘same’);
temp_D(:,:,j)=L2-L1;
imshow(uint8(255 * mat2gray(temp_D(:,:,j))));
L1=L2;
else
L2=conv2(temp_img,f,‘same’);
L2=conv2(L2,f’,‘same’);
temp_D(:,:,j)=L2-L1;
L1=L2;
if(j==level)
temp_img=L1(2:end-1,2:end-1);
end
imshow(uint8(255 * mat2gray(temp_D(:,:,j))));
end
end
D{i}=temp_D;
temp_img=temp_img(1:2:end,1:2:end);
temp_img=padarray(temp_img,[1,1],‘replicate’);
end
%% Keypoint Localistaion
% search each pixel in the DoG map to find the extreme point
interval=level-1;
extrema=[];
for i=1:octave
[m,n,~]=size(D{i});
m=m-2;
n=n-2;
volume=mn/(4^(i-1));
for k=2:interval
for j=1:volume
% starter=D(ceil(i/m)+1,mod(i-1,n)+1+1,j);
sub=D{i}(ceil(j/m):ceil(j/m)+2,mod(j-1,n)+1:mod(j-1,n)+3,k-1:k+1);
large=max(max(max(sub)));
little=min(min(min(sub)));
if(largeD{i}(ceil(j/m)+1,mod(j-1,n)+1+1,k))
temp=[i,k,j,1];
extrema=[extrema,temp];
end
if(littleD{i}(ceil(j/m)+1,mod(j-1,n)+1+1,k))
temp=[i,k,j,-1];
extrema=[extrema,temp];
end
end
end
end
%% accurate keypoint localization
%eliminate the point with low contrast or poorly localised on an edge
% x:|,y:-- x is for vertial and y is for horizontal
% value comes from the paper.
threhold=0.03;
r=10;
extr_volume=length(extrema)/4;
[m,n]=size(img);
for i=1:extr_volume
x=floor((extrema(4*(i-1)+3)-1)/(m/(2^(extrema(4*(i-1)+1)-2))))+1;
y=mod((extrema(4*(i-1)+3)-1),n/(2^(extrema(4*(i-1)+1)-2)))+1;
rx=x+1;
ry=y+1;
rz=extrema(4*(i-1)+2);
z=D{extrema(4*(i-1)+1)}(rx,ry,rz);
if(abs(z)<threhold)
extrema(4*(i-1)+4)=0;
else
Dxx=D{extrema(4*(i-1)+1)}(rx-1,ry,rz)+D{extrema(4*(i-1)+1)}(rx+1,ry,rz)-2D{extrema(4(i-1)+1)}(rx,ry,rz);
Dyy=D{extrema(4*(i-1)+1)}(rx,ry-1,rz)+D{extrema(4*(i-1)+1)}(rx,ry+1,rz)-2D{extrema(4(i-1)+1)}(rx,ry,rz);
Dxy=D{extrema(4*(i-1)+1)}(rx-1,ry-1,rz)+D{extrema(4*(i-1)+1)}(rx+1,ry+1,rz)-D{extrema(4*(i-1)+1)}(rx-1,ry+1,rz)-D{extrema(4*(i-1)+1)}(rx+1,ry-1,rz);
deter=DxxDyy-DxyDxy;
R=(Dxx+Dyy)/deter;
R_threshold=(r+1)^2/r;
if(deter<0||R>R_threshold)
extrema(4*(i-1)+4)=0;
end

end
1

end
flag=[];
for i=1:1:extr_volume
if(extrema(4*(i-1)+4)0)
flag=[flag,(4*(i-1)+1:4i)];
end
end
extrema(flag)=[];
extr_volume=length(extrema)/4;
% eliminate the points on the image edge.
flag=[];
for i=1:extr_volume
x=floor((extrema(4(i-1)+3)-1)/(m/(2^(extrema(4*(i-1)+1)-2))))+1;
y=mod((extrema(4*(i-1)+3)-1),n/(2^(extrema(4*(i-1)+1)-2)))+1;
if(x1||x==m/(2^{(extrema(4*(i-1)+1)-2))||y==1||y==n/(2}(extrema(4*(i-1)+1)-2)))
flag=[flag,(4*(i-1)+1:4i)];
end
end
extrema(flag)=[];
extr_volume=length(extrema)/4;
figure(2)
imshow(origin);
for i=1:extr_volume
x=floor((extrema(4(i-1)+3)-1)/(m/(2^(extrema(4*(i-1)+1)-2))))+1;
y=mod((extrema(4*(i-1)+3)-1),n/(2^(extrema(4*(i-1)+1)-2)))+1;
rx=x/2^(octave-1-extrema(4*(i-1)+1));
ry=y/2^(octave-1-extrema(4*(i-1)+1));
text(ry,rx,‘',‘Color’,‘green’);
end
hold on
%% pre-compute eigenspace of each extre point
flag=[];
A=[];
for i=1:extr_volume
x=floor((extrema(4(i-1)+3)-1)/(m/(2^(extrema(4*(i-1)+1)-2))))+1;
y=mod((extrema(4*(i-1)+3)-1),n/(2^(extrema(4*(i-1)+1)-2)))+1;
rx=x+1;
ry=y+1;
rz=extrema(4*(i-1)+2);
width=41;
if(x>(width/2)&&y>(width/2)&&x<(m/2^{(extrema(4*(i-1)+1)-2)-width/2-2)&&y<(n/2}(extrema(4*(i-1)+1)-2)-width/2-2))
patch=D{extrema(4*(i-1)+1)}(rx-(width-1)/2:rx+(width-1)/2,ry-(width-1)/2:ry+(width-1)/2,rz);
[Dx,Dy]=gradient(patch);
G=zeros(1,2widthwidth);
j=1;
for p=1:width
for q=1:width
G(j)=Dx(p,q);
G(j+1)=Dy(p,q);
j=j+2;
end
end
A=[A;G];
else
flag=[flag,(4*(i-1)+1:4i)];
end
end
extrema(flag)=[];
extr_volume=length(extrema)/4;
[p,q]=size(A);
M=A-repmat(mean(A,1),p,1);
conv=M’M;
[V,~]=eig(conv);
N=20;
project=V(:,(end-N+1):end)';
%% Orientation Assignment(Main orientations assignment)
kpori=[];
flag=[];
for i=1:extr_volume
%search in the certain scale
sigma=1.6sqrt(2)^(extrema(4(i-1)+1));
width=2round(31.5sigma);
count=1;
x=floor((extrema(4(i-1)+3)-1)/(m/(2^(extrema(4*(i-1)+1)-2))))+1;
y=mod((extrema(4*(i-1)+3)-1),n/(2^(extrema(4*(i-1)+1)-2)))+1;
%make sure the point in the searchable area
if(x>(width/2)&&y>(width/2)&&x<(m/2^{(extrema(4*(i-1)+1)-2)-width/2-2)&&y<(n/2}(extrema(4*(i-1)+1)-2)-width/2-2))
rx=x+1;
ry=y+1;
rz=extrema(4*(i-1)+2);
reg_volume=widthwidth;%3? thereom
% make weight matrix
weight=zeros(width,width);
for p=1:width
for q=1:width
weight(p,q)=exp(-((p-width/2)^{2+(q-width/2)}2)/(1.5sigma));
end
end
%calculate region pixels’ magnitude and region orientation
reg_mag=zeros(1,count);
reg_theta=zeros(1,count);
for l=(rx-width/2):(rx+width/2-1)
for k=(ry-width/2):(ry+width/2-1)
reg_mag(count)=sqrt((D{extrema(4*(i-1)+1)}(l+1,k,rz)-D{extrema(4*(i-1)+1)}(l-1,k,rz))^{2+(D{extrema(4*(i-1)+1)}(l,k+1,rz)-D{extrema(4*(i-1)+1)}(l,k-1,rz))}2);
reg_theta(count)=atan2((D{extrema(4*(i-1)+1)}(l,k+1,rz)-D{extrema(4*(i-1)+1)}(l,k-1,rz)),(D{extrema(4*(i-1)+1)}(l+1,k,rz)-D{extrema(4*(i-1)+1)}(l-1,k,rz)))*(180/pi);
count=count+1;
end
end

⛄四、运行结果

⛄五、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.

3 备注
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