FingerPrints.rar_matlab 指纹_matlab 指纹识别_ridgesegment_指纹_识别

% RIDGEFILTER - enhances fingerprint image via oriented filters % % Function to enhance fingerprint image via oriented filters % % Usage: % newim = ridgefilter(im, orientim, freqim, kx, ky, showfilter) % % Arguments: % im - Image to be processed. % orientim - Ridge orientation image, obtained from RIDGEORIENT. % freqim - Ridge frequency image, obtained from RIDGEFREQ. % kx, ky - Scale factors specifying the filter sigma relative % to the wavelength of the filter. This is done so % that the shapes of the filters are invariant to the % scale. kx controls the sigma in the x direction % which is along the filter, and hence controls the % bandwidth of the filter. ky controls the sigma % across the filter and hence controls the % orientational selectivity of the filter. A value of % 0.5 for both kx and ky is a good starting point. % showfilter - An optional flag 0/1. When set an image of the % largest scale filter is displayed for inspection. % % Returns: % newim - The enhanced image % % See also: RIDGEORIENT, RIDGEFREQ, RIDGESEGMENT % Reference: % Hong, L., Wan, Y., and Jain, A. K. Fingerprint image enhancement: % Algorithm and performance evaluation. IEEE Transactions on Pattern % Analysis and Machine Intelligence 20, 8 (1998), 777 789. % Peter Kovesi % School of Computer Science & Software Engineering % The University of Western Australia % pk at csse uwa edu au % http://www.csse.uwa.edu.au/~pk % % January 2005 function newim = ridgefilter(im, orient, freq, kx, ky, showfilter) if nargin == 5 showfilter = 0; end angleInc = 3; % Fixed angle increment between filter orientations in % degrees. This should divide evenly into 180 im = double(im); [rows, cols] = size(im); newim = zeros(rows,cols); [validr,validc] = find(freq > 0); % find where there is valid frequency data. ind = sub2ind([rows,cols], validr, validc); % Round the array of frequencies to the nearest 0.01 to reduce the % number of distinct frequencies we have to deal with. freq(ind) = round(freq(ind)*100)/100; % Generate an array of the distinct frequencies present in the array % freq unfreq = unique(freq(ind)); % Generate a table, given the frequency value multiplied by 100 to obtain % an integer index, returns the index within the unfreq array that it % corresponds to freqindex = ones(100,1); for k = 1:length(unfreq) freqindex(round(unfreq(k)*100)) = k; end % Generate filters corresponding to these distinct frequencies and % orientations in 'angleInc' increments. filter = cell(length(unfreq),180/angleInc); sze = zeros(length(unfreq),1); for k = 1:length(unfreq) sigmax = 1/unfreq(k)*kx; sigmay = 1/unfreq(k)*ky; sze(k) = round(3*max(sigmax,sigmay)); [x,y] = meshgrid(-sze(k):sze(k)); reffilter = exp(-(x.^2/sigmax^2 + y.^2/sigmay^2)/2)... .*cos(2*pi*unfreq(k)*x); % Generate rotated versions of the filter. Note orientation % image provides orientation *along* the ridges, hence +90 % degrees, and imrotate requires angles +ve anticlockwise, hence % the minus sign. for o = 1:180/angleInc filter{k,o} = imrotate(reffilter,-(o*angleInc+90),'bilinear','crop'); end end if showfilter % Display largest scale filter for inspection figure(7), imshow(filter{1,end},[]); title('filter'); end % Find indices of matrix points greater than maxsze from the image % boundary maxsze = sze(1); finalind = find(validr>maxsze & validr<rows-maxsze & ... validc>maxsze & validc<cols-maxsze); % Convert orientation matrix values from radians to an index value % that corresponds to round(degrees/angleInc) maxorientindex = round(180/angleInc); orientindex = round(orient/pi*180/angleInc); i = find(orientindex < 1); orientindex(i) = orientindex(i)+maxorientindex; i = find(orientindex > maxorientindex); orientindex(i) = orientindex(i)-maxorientindex; % Finally do the filtering for k = 1:length(finalind) r = validr(finalind(k)); c = validc(finalind(k)); % find filter corresponding to freq(r,c) filterindex = freqindex(round(freq(r,c)*100)); s = sze(filterindex); newim(r,c) = sum(sum(im(r-s:r+s, c-s:c+s).*filter{filterindex,orientindex(r,c)})); end