www.pudn.com > MatlabSIFT.zip > filter_laplacian.m

%///////////////////////////////////////////////////////////////////////////////////////////// 
% Author : Scott Ettinger 
% 
% filter_gaussian(img, order, sig)   
% 
% The image is first padded with the outer image data enough times to allow for the size of the  
% filter used.  
 
function image_out = filter_gaussian(img,order,sig) 
 
h1 = gauss1d(order,sig); %create filter coefficient matrix 
h2 = conv2(h1, [.5 0 -.5]); 
h3 = conv2(h2,h2); 
h3 = h3/sum(abs(h3)); 
 
order = length(h3); 
 
img2 = img; 
for i=1:floor(order/2)  %pad image borders with enough for filter order 
     
    [h,w] = size(img2); 
    
    img2 = [img2(1,1)  img2(1,:)  img2(1,w); 
            img2(:,1)  img2       img2(:,w); 
            img2(h,1)  img2(h,:)  img2(h,w)]; 
end  
    
 
     
image_out = conv2(img2,h3','valid'); % do the filtering 
image_out = image_out(:,floor(order/2)+1:end-floor(order/2)); 
image_out2 = conv2(img2,h3,'valid'); % do the filtering 
image_out2 = image_out2(floor(order/2)+1:end-floor(order/2),:); 
image_out = -image_out-image_out2; 
 
 
%///////////////////////////////////////////////////////////////////////////////////////// 
 
function f = gauss1d(order,sig) 
 
f=0; 
i=0; 
j=0; 
 
%generate gaussian coefficients  
 
for x = -fix(order/2):1:fix(order/2) 
    i = i + 1; 
    f(i) = 1/2/pi*exp(-((x^2)/(2*sig^2))); 
end 
 
f = f / sum(sum(f)); %normalize filter 
 
 
%///////////////////////////////////////////////////////////////////////////////////////// 
 
function f = gauss2d(order,sig) 
 
f=0; 
i=0; 
j=0; 
 
%generate gaussian coefficients  
 
for x = -fix(order/2):1:fix(order/2) 
    j=j+1; 
    i=0; 
    for y = -fix(order/2):1:fix(order/2) 
        i=i+1; 
        f(i,j) = 1/2/pi*exp(-((x^2+y^2)/(2*sig^2))); 
    end 
end 
 
f = f / sum(sum(f)); %normalize filter