matlab写的指纹识别代码包括图像修复

% RIDGEORIENT - Estimates the local orientation of ridges in a fingerprint % % Usage: [orientim, reliability, coherence] = ridgeorientation(im, gradientsigma,... % blocksigma, ... % orientsmoothsigma) % % Arguments: im - A normalised input image. % gradientsigma - Sigma of the derivative of Gaussian % used to compute image gradients. % blocksigma - Sigma of the Gaussian weighting used to % sum the gradient moments. % orientsmoothsigma - Sigma of the Gaussian used to smooth % the final orientation vector field. % Optional: if ommitted it defaults to 0 % % Returns: orientim - The orientation image in radians. % Orientation values are +ve clockwise % and give the direction *along* the % ridges. % reliability - Measure of the reliability of the % orientation measure. This is a value % between 0 and 1. I think a value above % about 0.5 can be considered 'reliable'. % reliability = 1 - Imin./(Imax+.001); % coherence - A measure of the degree to which the local % area is oriented. % coherence = ((Imax-Imin)./(Imax+Imin)).^2; % % With a fingerprint image at a 'standard' resolution of 500dpi suggested % parameter values might be: % % [orientim, reliability] = ridgeorient(im, 1, 3, 3); % % See also: RIDGESEGMENT, RIDGEFREQ, RIDGEFILTER % May 2003 Original version by Raymond Thai, % January 2005 Reworked by Peter Kovesi % October 2011 Added coherence computation and orientsmoothsigma made optional % % School of Computer Science & Software Engineering % The University of Western Australia % pk at csse uwa edu au % http://www.csse.uwa.edu.au/~pk function [orientim, reliability, coherence] = ... ridgeorient(im, gradientsigma, blocksigma, orientsmoothsigma) if ~exist('orientsmoothsigma', 'var'), orientsmoothsigma = 0; end [rows,cols] = size(im); % Calculate image gradients. sze = fix(6*gradientsigma); if ~mod(sze,2); sze = sze+1; end f = fspecial('gaussian', sze, gradientsigma); % Generate Gaussian filter. [fx,fy] = gradient(f); % Gradient of Gausian. Gx = filter2(fx, im); % Gradient of the image in x Gy = filter2(fy, im); % ... and y % Estimate the local ridge orientation at each point by finding the % principal axis of variation in the image gradients. Gxx = Gx.^2; % Covariance data for the image gradients Gxy = Gx.*Gy; Gyy = Gy.^2; % Now smooth the covariance data to perform a weighted summation of the % data. sze = fix(6*blocksigma); if ~mod(sze,2); sze = sze+1; end f = fspecial('gaussian', sze, blocksigma); Gxx = filter2(f, Gxx); Gxy = 2*filter2(f, Gxy); Gyy = filter2(f, Gyy); % Analytic solution of principal direction denom = sqrt(Gxy.^2 + (Gxx - Gyy).^2) + eps; sin2theta = Gxy./denom; % Sine and cosine of doubled angles cos2theta = (Gxx-Gyy)./denom; if orientsmoothsigma sze = fix(6*orientsmoothsigma); if ~mod(sze,2); sze = sze+1; end f = fspecial('gaussian', sze, orientsmoothsigma); cos2theta = filter2(f, cos2theta); % Smoothed sine and cosine of sin2theta = filter2(f, sin2theta); % doubled angles end orientim = pi/2 + atan2(sin2theta,cos2theta)/2; % Calculate 'reliability' of orientation data. Here we calculate the % area moment of inertia about the orientation axis found (this will % be the minimum inertia) and an axis perpendicular (which will be % the maximum inertia). The reliability measure is given by % 1.0-min_inertia/max_inertia. The reasoning being that if the ratio % of the minimum to maximum inertia is close to one we have little % orientation information. Imin = (Gyy+Gxx)/2 - (Gxx-Gyy).*cos2theta/2 - Gxy.*sin2theta/2; Imax = Gyy+Gxx - Imin; reliability = 1 - Imin./(Imax+.001); coherence = ((Imax-Imin)./(Imax+Imin)).^2; % Finally mask reliability to exclude regions where the denominator % in the orientation calculation above was small. Here I have set % the value to 0.001, adjust this if you feel the need reliability = reliability.*(denom>.001);