gen_dgauss.m

%
% take gradient for gaussian template
%
% Created by Rui Zhao, on May 20, 2013. 
% This code is release under BSD license, 
% any problem please contact Rui Zhao rzhao@ee.cuhk.edu.hk
%
% Please cite as
% Rui Zhao, Wanli Ouyang, and Xiaogang Wang. Unsupervised Salience Learning
% for Person Re-identification. In IEEE Conference of Computer Vision and
% Pattern Recognition (CVPR), 2013. 
%

function [GX,GY]=gen_dgauss(sigma)

% laplacian of size sigma
%f_wid = 4 * floor(sigma);
%G = normpdf(-f_wid:f_wid,0,sigma);
%G = G' * G;

if(nargin < 1)
    
    sigma = 1;
    
end

G       = gen_gauss(sigma);
[GX,GY] = gradient(G); 

GX      = GX * 2 ./ sum(sum(abs(GX)));
GY      = GY * 2 ./ sum(sum(abs(GY)));



function G=gen_gauss(sigma)

if all(size(sigma)==[1, 1])
    % isotropic gaussian
	f_wid = 4 * ceil(sigma) + 1;
    G = fspecial('gaussian', f_wid, sigma);
%	G = normpdf(-f_wid:f_wid,0,sigma);
%	G = G' * G;
else
    % anisotropic gaussian
    f_wid_x = 2 * ceil(sigma(1)) + 1;
    f_wid_y = 2 * ceil(sigma(2)) + 1;
    G_x = normpdf(-f_wid_x:f_wid_x,0,sigma(1));
    G_y = normpdf(-f_wid_y:f_wid_y,0,sigma(2));
    G = G_y' * G_x;
end