📄 testfin.m
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% TESTFIN %% Function to demonstrate use of fingerprint code%% Usage: [newim, binim, mask, reliability] = testfin(im);%% Argument: im - Fingerprint image to be enhanced.%% Returns: newim - Ridge enhanced image.% binim - Binary version of enhanced image.% mask - Ridge-like regions of the image% reliability - 'Reliability' of orientation data% 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 2005function [newim, binim, mask, reliability] = testfin(im) if nargin == 0 im = imread('finger.png'); end % Identify ridge-like regions and normalise image blksze = 16; thresh = 0.1; [normim, mask] = ridgesegment(im, blksze, thresh); show(normim,1); % Determine ridge orientations [orientim, reliability] = ridgeorient(normim, 1, 5, 5); plotridgeorient(orientim, 20, im, 2) show(reliability,6) % Determine ridge frequency values across the image blksze = 36; [freq, medfreq] = ridgefreq(normim, mask, orientim, blksze, 5, 5, 15); show(freq,3) % Actually I find the median frequency value used across the whole % fingerprint gives a more satisfactory result... freq = medfreq.*mask; % Now apply filters to enhance the ridge pattern newim = ridgefilter(normim, orientim, freq, 0.5, 0.5, 1); show(newim,4); % Binarise, ridge/valley threshold is 0 binim = newim > 0; show(binim,5); % Display binary image for where the mask values are one and where % the orientation reliability is greater than 0.5 show(binim.*mask.*(reliability>0.5), 7)
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