testfin.m

来自「hopfield neural network for binary image」· M 代码 · 共 59 行

% 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 2005


function [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)