probelm in segmentation in fingerprint recognition

[SpellChainz]

fingerprint segmentation code

hi....
i m doing a project on fingerprint recgnition....currently i m working on a thesis that i have found from the net....i m facing problem in understanding segmenattion code...i have searched net for the relavant material but didnt find anything related to it...kindly if sum1 make me understand the following code......i ll b very thakful ....


function [p,z] = direction(image,blocksize,noShow)
% DIRECTION cacluates the local flow orientation in each local window
% with size (blocksize x blocksize)
% direction(grayScaleFingerprintImage,blocksize,graphicalShowDisableFlag)
% return p ROI bound
% return z ROI area
%Honors Project 2001~2002
%wuzhili 99050056
%comp sci HKBU
%last update 19/April/2002

%image=adaptiveThres(image,16,0);

[w,h] = size(image);
direct = zeros(w,h);
gradient_times_value = zeros(w,h);
gradient_sq_minus_value = zeros(w,h);
gradient_for_bg_under = zeros(w,h);

W = blocksize;
theta = 0;
sum_value = 1;
bg_certainty = 0;

blockIndex = zeros(ceil(w/W),ceil(h/W));
%directionIndex = zeros(ceil(w/W),ceil(h/W));


times_value = 0;
minus_value = 0;

center = [];

%Note that the image coordinate system is
%x axis towards bottom and y axis towards right

filter_gradient = fspecial('sobel');
%to get x gradient
I_horizontal = filter2(filter_gradient,image);

%to get y gradient
filter_gradient = transpose(filter_gradient);
I_vertical = filter2(filter_gradient,image);


gradient_times_value=I_horizontal.*I_vertical;
gradient_sq_minus_value=(I_vertical-I_horizontal).*(I_vertical+I_horizontal);
gradient_for_bg_under = (I_horizontal.*I_horizontal) + (I_vertical.*I_vertical);


for i=1:W:w
for j=1:W:h
if j+W-1 < h & i+W-1 < w
times_value = sum(sum(gradient_times_value(i:i+W-1, j:j+W-1)));
minus_value = sum(sum(gradient_sq_minus_value(i:i+W-1, j:j+W-1)));
sum_value = sum(sum(gradient_for_bg_under(i:i+W-1, j:j+W-1)));

bg_certainty = 0;
theta = 0;

if sum_value ~= 0 & times_value ~=0
%if sum_value ~= 0 & minus_value ~= 0 & times_value ~= 0
bg_certainty = (times_value*times_value + minus_value*minus_value)/(W*W*sum_value);

if bg_certainty > 0.05
blockIndex(ceil(i/W),ceil(j/W)) = 1;

%tan_value = atan2(minus_value,2*times_value);
tan_value = atan2(2*times_value,minus_value);


theta = (tan_value)/2 ;
theta = theta+pi/2;
%now the theta is within [0,pi]

%directionIndex(ceil(i/W),ceil(j/W)) = theta;
%center = [center;[round(i + (W-1)/2),round(j + (W-1)/2),theta,bg_certainty]];
center = [center;[round(i + (W-1)/2),round(j + (W-1)/2),theta]];
end;
end;
end;
times_value = 0;
minus_value = 0;
sum_value = 0;

end;
end;



if nargin == 2
imagesc(direct);

hold on;
[u,v] = pol2cart(center,3),8);
quiver(center,2),center,1),u,v,0,'g');
hold off;
end;


x = bwlabel(blockIndex,4);
%map = [0 0 0;jet(3)];
%figure
%imshow(x+1,map,'notruesize')

y = bwmorph(x,'close');
%figure
%imshow(y,map,'notruesize');

%z is the region of interest (ROI)
%with the index format

z = bwmorph(y,'open');
%figure
%imshow(z,map,'notruesize');

%p is the bound of ROI

p = bwperim(z);

 

[leekk8]

orientation (image, blocksize, noshow)

By only looking at the code without any algorithm, it's impossible to understand the code, unless you're very familiar with the algorithm. I believe this code is written based on the segmentation algorithm. You must have the algorithm then only you can understand the code.