www.pudn.com > Gabor_2dpca_pca.rar > gaborpalm.m

% function [output]=gaborpalm(imagein) 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
% this function is used to create gaborpalms and then gained the sampled matrix  
% wenchangzhi 2006/9/17 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
% clear,close all; 
clc; 
 
A=[]; 
scale=5;  %  denote the scale of the gabor wavelet 
orientation=4;  % denote the orientation of the gabor wavelet 
kmax=pi/2;   % the highest frequency with the smallest kernel size 
sigma=pi*2;  % the parameter :theta  
mask_size=128;  % for every kernel the size is 64*64% this is the size of Gabor mask 
f=sqrt(2);   % f is the spacing factor 
sig=sigma*sigma; % the squre of theta 
offset=mask_size/2;  
 
% nargin=8; 
 
imagein=double(imread('P_30_1.bmp')); 
figure(3); 
imshow(uint8(imagein)); 
 
k=1; 
for v=0:(scale-1) 
    for u=0:(orientation-1) 
        kv=kmax/f^v; 
        phiu=u*pi/4; 
        kv_mag=kv*kv; 
        gabor_kel=zeros(mask_size,mask_size);         
            for x=0:(mask_size-1)                   
                for y=0:(mask_size-1)                 
                    i=x-offset;                 
                    j=y-offset;                 
                    mag=i*i+j*j;                 
                    gabor_kel(x+1,y+1)=kv_mag/sig*exp(-0.5*kv_mag*mag/sig)*(exp(sqrt(-1)*kv*(i*cos(phiu)+j*sin(phiu))-exp(-1.0*sig/2.0)));                    
                end           %the gabor kernel       
            end     
%               result=abs(fftshift(ifft2((fft2(imagein)).*fft2(gabor_kel))));    % 与输入图像相卷积得到Gabopam     
              
              result=abs(ifft2(fft2(imagein).*fft2(fftshift(gabor_kerl))));    % 与输入图像相卷积得到Gabopam     
 
              figure(1); 
              subplot(5,4,k); 
              imshow(result,[]); 
              figure(2); 
              subplot(5,4,k); 
              imshow(real(gabor_kel),[]); 
              k=k+1; 
               
               
%               result_sample=result(1:nargin:mask_size,1:nargin:mask_size);  %对得到的结果进行下采样，采样因子为4 
%                
%               m=mean2(result_sample); 
%               s=std2(result_sample); 
%               result_norm=(result_sample-m)/s;  % 对得到的特征向量进行归一化 
%                
%               result_norm=result_norm(1:end); % 变为1*256的行向量  
%               A=[A result_norm'];   %特征向量的长度为 20*256（20=4*5） 
       end 
end   
% output=A;