matlab - Implementing a fingerprint quality measure? -
i trying implement spatial domain fingerprint quality analysis. quality measure shown in below:
i able implement till equation 9. next step calculate weight shown in equation 11. when tried implementing weights zero. of great if me fix issue. have attached code below:
clc;clear all;close all; img = imread('c:\users\shrey\google drive\quality\testdatabase\fvc2004\db1_b\101_2.tif'); b = 2; br=1; img=finger_segment(img); img(img==0.5)=0; [gx,gy]=gradient(img); = 1 :b: size(img,1) j = 1 :b: size(img,2) % gradients blockwise gx1 = gx(i:min(i+b-1,size(gx,1)),j:min(j+b-1,size(gx,2))); gy1 = gy(i:min(i+b-1,size(gy,1)),j:min(j+b-1,size(gy,2))); temp = img(i:min(i+b-1,size(img,1)),j:min(j+b-1,size(img,2))); if (any(any(temp)) == 1) k(br) = coherence_grad_3(gx1,gy1,b); temp2=temp/sum(temp(:)); % centroid calculation [r1,c1]=size(temp); [i,j]=ndgrid(1:r1,1:c1); li(br,:)=[dot(i(:),temp2(:)),dot(j(:),temp2(:))]; li1(br,1)=li(br,1)+ (i-1); li1(br,2)=li(br,2)+ (j-1); br = br + 1; end end end %% finding centroid of whole image k(isnan(k))=0; mask = zeros(size(img)); mask(img>0)=1; % mask find centroid of foreground imgcent=mask/sum(mask(:)); [r1,c1]=size(mask); [i,j]=ndgrid(1:r1,1:c1); lc=[dot(i(:),imgcent(:)),dot(j(:),imgcent(:))]; %% relative weight sub=bsxfun(@minus, lc, li); c=1:br-1 num(c) =(norm(sub(c,:))); end c=1:br-1 wi(c) = exp(-(num(c).^2)/2*5); end q = (1/br) * sum(wi.*k); functions above code % coherence matrix equation 7 code: function k1= coherence_grad_3(gx,gy,b) %untitled3 summary of function goes here % detailed explanation goes here j=0; = 1:size(gx,1) j = 1:size(gx,2) gs=[gx(i,j),gy(i,j)]'; j= gs* gs' + j; end end j= (j/(b.^2)); l = eig(j); k1 = ((l(1)-l(2)).^2)/((l(1)+l(2)).^2); end % segmentation program function f3=finger_segment(h) if(ndims(h)==3) h= rgb2gray(h); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % function segment fingerprint image using morphological processing. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% h=im2double(h); %%% step 1: feature extraction h1=rangefilt(h); %%% convert binary image h2=adaptivethreshold(h1,16,0.05,0); %%% step 2: morphological processing se = strel('disk', 6, 4); h3=imclose(~h2,se); h4=imopen(h3,se); h4 = imfill(h4,'holes'); se1=strel('square',12); h4=imerode(h4,se1); [m1,n1] = size(h); f=zeros(m1,n1); [l, num] = bwlabel(h4,8); max1=0; j=1:num [r, c] = find(bwlabel(l)==j); m=size(r,1); if (max1<m) id=j; max1=m; end end [r, c] = find(bwlabel(l)==id); s3=size(r,1); i=1:s3 f(r(i),c(i))=1; end %%% step 3: contour smoothing erf=boundaries(f); erf=erf{1}; z=frdescp(erf); z1=ifrdescp(z,50); x = round(z1(:, 1)); y = round(z1(:, 2)); if(min(x)<=0) x = x - min(x) + 1; end if (min(y)<=0) y = y - min(y) + 1; end zz=zeros(m1,n1); ii=1:size(z1,1) zz(x(ii),y(ii))=1; end se1=strel('square',3); zz=imdilate(zz,se1); [r1, c1] = find(zz==1); zz=imfill(zz,'holes'); i=1:m1 j=1:n1 if(zz(i,j)==1) f3(i,j)=h(i,j); else f3(i,j)=0.5; end end end function bw=adaptivethreshold(im,ws,c,tm) if (nargin<3) error('you must provide image im, window size ws, , c.'); elseif (nargin==3) tm=0; elseif (tm~=0 && tm~=1) error('tm must 0 or 1.'); end im=mat2gray(im); if tm==0 mim=imfilter(im,fspecial('average',ws),'replicate'); else mim=medfilt2(im,[ws ws]); end sim=mim-im-c; bw=im2bw(sim,0); bw=imcomplement(bw); function b = boundaries(bw, conn, dir) if nargin < 3 dir = 'cw'; end if nargin < 2 conn = 8; end l = bwlabel(bw, conn); numobjects = max(l(:)); if numobjects > 0 b = {zeros(0, 2)}; b = repmat(b, numobjects, 1); else b = {}; end lp = padarray(l, [1 1], 0, 'both'); m = size(lp, 1); if conn == 8 offsets = [-1, m - 1, m, m + 1, 1, -m + 1, -m, -m-1]; else offsets = [-1, m, 1, -m]; end if conn == 8 next_search_direction_lut = [8 8 2 2 4 4 6 6]; else next_search_direction_lut = [4 1 2 3]; end if conn == 8 next_direction_lut = [2 3 4 5 6 7 8 1]; else next_direction_lut = [2 3 4 1]; end start = -1; boundary = -2; scratch = zeros(100, 1); [rr, cc] = find((lp(2:end-1, :) > 0) & (lp(1:end-2, :) == 0)); rr = rr + 1; k = 1:length(rr) r = rr(k); c = cc(k); if (lp(r,c) > 0) & (lp(r - 1, c) == 0) & isempty(b{lp(r, c)}) idx = (c-1)*size(lp, 1) + r; = lp(idx); scratch(1) = idx; lp(idx) = start; numpixels = 1; currentpixel = idx; initial_departure_direction = []; done = 0; next_search_direction = 2; while ~done direction = next_search_direction; found_next_pixel = 0; k = 1:length(offsets) neighbor = currentpixel + offsets(direction); if lp(neighbor) ~= 0 if (lp(currentpixel) == start) & ... isempty(initial_departure_direction) initial_departure_direction = direction; elseif (lp(currentpixel) == start) & ... (initial_departure_direction == direction) done = 1; found_next_pixel = 1; break; end next_search_direction = ... next_search_direction_lut(direction); found_next_pixel = 1; numpixels = numpixels + 1; if numpixels > size(scratch, 1) scratch(2*size(scratch, 1)) = 0; end scratch(numpixels) = neighbor; if lp(neighbor) ~= start lp(neighbor) = boundary; end currentpixel = neighbor; break; end direction = next_direction_lut(direction); end if ~found_next_pixel numpixels = 2; scratch(2) = scratch(1); done = 1; end end [row, col] = ind2sub(size(lp), scratch(1:numpixels)); b{which} = [row - 1, col - 1]; end end if strcmp(dir, 'ccw') k = 1:length(b) b{k} = b{k}(end:-1:1, :); end end function z = frdescp(s) [np, nc] = size(s); if nc ~= 2 error('s must of size np-by-2.'); end if np/2 ~= round(np/2); s(end + 1, :) = s(end, :); np = np + 1; end x = 0:(np - 1); m = ((-1) .^ x)'; s(:, 1) = m .* s(:, 1); s(:, 2) = m .* s(:, 2); s = s(:, 1) + i*s(:, 2); z = fft(s); function s = ifrdescp(z, nd) np = length(z); if nargin == 1 | nd > np nd = np; end x = 0:(np - 1); m = ((-1) .^ x)'; d = round((np - nd)/2); z(1:d) = 0; z(np - d + 1:np) = 0; zz = ifft(z); s(:, 1) = real(zz); s(:, 2) = imag(zz); s(:, 1) = m.*s(:, 1); s(:, 2) = m.*s(:, 2); thanks in advance.
Comments
Post a Comment