2008-06-12 回答

function [ncut] = graphcuts(i,pad,maxval)

% function [ncut] = graphcuts(i)

% input: i image

% pad: spatial connectivity; eg. 3

% maxval: maximum image value

% output: ncut: binary map 0 or 1 corresponding to image segmentation

i = double(i); [h,w] = size(i);

% find weights between nodes i1 and i2, w = exp(a*abs(i1-i2));

% set a to have a weight of 0.01 for diff = maxval

a = log(0.01)/maxval; x = [0:maxval/100:maxval]'; y = exp(a*x);

figure;plot(x,y);xlabel('intensity diff');ylabel('weights'); title('weights')

ws = 2*pad + 1;

if(ws <= 3)

ws = 3;

end

%build the weight matrix

disp('building weight matrix'); close all; tic

wm = zeros(h*w,h*w); countwm = 0;

for kk = 1:w

for jj = 1:h

mask = logical(zeros(h,w));

cs = kk-pad; ce = kk+pad; rs = jj-pad; re = jj+pad;

if(cs<1)

cs = 1;

end;

if(ce>w)

ce = w;

end;

if(rs<1)

rs = 1;

end;

if(re>h)

re = h;

end;

mask(rs:re,cs:ce) = 1;

idx = find(mask==1);

p = abs(i(idx) - i(jj,kk)); p = exp(a*p);

countwm = countwm + 1; wm(countwm,idx) = p(:)';

end

end

ttime = toc; disp(sprintf('time for generating weight matrix = %f',ttime)); clear countwm

% weight between a node and iteself is 0

for jj = 1:h*w

wm(jj,jj) = 0;

end;

wm = sparse(wm);

% shi and malik algorithm: second smallest eigen vector

disp('finding eigen vector');

d = sum(wm,2); d = diag(d); tic

b = (d-wm); b = (b+b')/2; opts.disp = 0;

[v,d,flag] = eigs(b,d,2,'sa',opts); ttime = toc;

disp(sprintf('time for finding eigen vector = %f',ttime)); clear opts

y = v(:,2);

ncut = reshape(y,h,w);

ncut = ncut > 0;