ransac.zip_RANSAC matlab_matlab ransac误_ransac算法matlab_误匹配_误匹配算

% MATCHBYMONOGENICPHASE - match image feature points using monogenic phase data % % Function generates putative matches between previously detected % feature points in two images by looking for points that have minimal % differences in monogenic phase data within windows surrounding each point. % Only points that correlate most strongly with each other in *both* % directions are returned. This is a simple-minded N^2 comparison. % % This matcher performs rather well relative to normalised greyscale % correlation. Typically there are more putative matches found and fewer % outliers. There is a greater computational cost in the pre-filtering stage % but potentially the matching stage is much faster as each pixel is effectively % encoded with only 3 bits. (Though this potential speed is not realized in this % implementation) % % Usage: [m1,m2] = matchbymonogenicphase(im1, p1, im2, p2, w, dmax, ... % nscale, minWaveLength, mult, sigmaOnf) % % Arguments: % im1, im2 - Images containing points that we wish to match. % p1, p2 - Coordinates of feature pointed detected in im1 and % im2 respectively using a corner detector (say Harris % or phasecong2). p1 and p2 are [2xnpts] arrays though % p1 and p2 are not expected to have the same number % of points. The first row of p1 and p2 gives the row % coordinate of each feature point, the second row % gives the column of each point. % w - Window size (in pixels) over which the phase bit codes % around each feature point are matched. This should % be an odd number. % dmax - Maximum search radius for matching points. Used to % improve speed when there is little disparity between % images. Even setting it to a generous value of 1/4 of % the image size gives a useful speedup. % nscale - Number of filter scales. % minWaveLength - Wavelength of smallest scale filter. % mult - Scaling factor between successive filters. % sigmaOnf - Ratio of the standard deviation of the Gaussian % describing the log Gabor filter's transfer function in % the frequency domain to the filter center frequency. % % % Returns: % m1, m2 - Coordinates of points selected from p1 and p2 % respectively such that (putatively) m1(:,i) matches % m2(:,i). m1 and m2 are [2xnpts] arrays defining the % points in each of the images in the form [row;col]. % % % I have had good success with the folowing parameters: % % w = 11; Window size for correlation matching, 7 or greater % seems fine. % dmax = 50; % nscale = 1; Just one scale can give very good results. Adding % another scale doubles computation % minWaveLength = 10; % mult = 4; This is irrelevant if only one scale is used. If you do % use more than one scale try values in the range 2-4. % sigmaOnf = .2; This results in a *very* large bandwidth filter. A % large bandwidth seems to be very important in the % matching performance. % % See Also: MATCHBYCORRELATION, MONOFILT % Copyright (c) 2005 Peter Kovesi % School of Computer Science & Software Engineering % The University of Western Australia % http://www.csse.uwa.edu.au/ % % Permission is hereby granted, free of charge, to any person obtaining a copy % of this software and associated documentation files (the "Software"), to deal % in the Software without restriction, subject to the following conditions: % % The above copyright notice and this permission notice shall be included in % all copies or substantial portions of the Software. % % The Software is provided "as is", without warranty of any kind. % May 2005 - Original version adapted from matchbycorrelation.m function [m1,m2,cormat] = matchbymonogenicphase(im1, p1, im2, p2, w, dmax, ... nscale, minWaveLength, mult, sigmaOnf) orientWrap = 0; [f1, h1f1, h2f1, A1] = ... monofilt(im1, nscale, minWaveLength, mult, sigmaOnf, orientWrap); [f2, h1f2, h2f2, A2] = ... monofilt(im2, nscale, minWaveLength, mult, sigmaOnf, orientWrap); % Normalise filter outputs to unit vectors (should also have masking for % unreliable filter outputs) for s = 1:nscale % f1{s} = f1{s}./A1{s}; f2{s} = f2{s}./A2{s}; % h1f1{s} = h1f1{s}./A1{s}; h1f2{s} = h1f2{s}./A2{s}; % h2f1{s} = h2f1{s}./A1{s}; h2f2{s} = h2f2{s}./A2{s}; % Try quantizing oriented phase vector to 8 octants to see what % effect this has (Performance seems to be reduced only slightly) f1{s} = sign(f1{s}); f2{s} = sign(f2{s}); h1f1{s} = sign(h1f1{s}); h1f2{s} = sign(h1f2{s}); h2f1{s} = sign(h2f1{s}); h2f2{s} = sign(h2f2{s}); end % Generate correlation matrix cormat = correlationmatrix(f1, h1f1, h2f1, p1, ... f2, h1f2, h2f2, p2, w, dmax); [corrows,corcols] = size(cormat); % Find max along rows give strongest match in p2 for each p1 [mp2forp1, colp2forp1] = max(cormat,[],2); % Find max down cols give strongest match in p1 for each p2 [mp1forp2, rowp1forp2] = max(cormat,[],1); % Now find matches that were consistent in both directions p1ind = zeros(1,length(p1)); % Arrays for storing matched indices p2ind = zeros(1,length(p2)); indcount = 0; for n = 1:corrows if rowp1forp2(colp2forp1(n)) == n % consistent both ways indcount = indcount + 1; p1ind(indcount) = n; p2ind(indcount) = colp2forp1(n); end end % Trim arrays of indices of matched points p1ind = p1ind(1:indcount); p2ind = p2ind(1:indcount); % Extract matched points from original arrays m1 = p1(:,p1ind); m2 = p2(:,p2ind); %------------------------------------------------------------------------- % Function that does the work. This function builds a 'correlation' matrix % that holds the correlation strength of every point relative to every other % point. While this seems a bit wasteful we need all this data if we want % to find pairs of points that correlate maximally in both directions. function cormat = correlationmatrix(f1, h1f1, h2f1, p1, ... f2, h1f2, h2f2, p2, w, dmax) if mod(w, 2) == 0 | w < 3 error('Window size should be odd and >= 3'); end r = (w-1)/2; % 'radius' of correlation window [rows1, npts1] = size(p1); [rows2, npts2] = size(p2); if rows1 ~= 2 | rows2 ~= 2 error('Feature points must be specified in 2xN arrays'); end % Reorganize monogenic phase data into a 4D matrices for convenience [im1rows,im1cols] = size(f1{1}); [im2rows,im2cols] = size(f2{1}); nscale = length(f1); phase1 = zeros(im1rows,im1cols,nscale,3); phase2 = zeros(im2rows,im2cols,nscale,3); for s = 1:nscale phase1(:,:,s,1) = f1{s}; phase1(:,:,s,2) = h1f1{s}; phase1(:,:,s,3) = h2f1{s}; phase2(:,:,s,1) = f2{s}; phase2(:,:,s,2) = h1f2{s}; phase2(:,:,s,3) = h2f2{s}; end % Initialize correlation matrix values to -infinity cormat = repmat(-inf, npts1, npts2); % For every feature point in the first image extract a window of data % and correlate with a window corresponding to every feature point in % the other image. Any feature point less than distance 'r' from the % boundary of an image is not considered. % Find indices