1 | % By Philip Torr 2002
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2 | % copyright Microsoft Corp.
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3 |
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4 | %this is a set of functions for minimizing F
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5 |
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6 | function [f, f_sq_errors, n_inliers,inlier_index,F] = torr_estimateF( matches, m3, f_optim_parameters, method, set_rank2, f_init)
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7 |
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8 | no_matches = length(matches);
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9 | x1 = matches(:,1);
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10 | y1 = matches(:,2);
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11 | x2 = matches(:,3);
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12 | y2 = matches(:,4);
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13 |
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14 |
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15 | if nargin <5
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16 | set_rank2 = 0;
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17 | end
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18 |
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19 | switch lower(method)
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20 | %as in ransac and torr mlesac/mapsac papers
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21 | case {'mlesac',0}
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22 | no_samp = f_optim_parameters(1);
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23 | T = f_optim_parameters(2);
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24 | f = torr_mlesac_F(x1,y1,x2,y2, no_matches, m3, no_samp, T);
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25 | %function f = mlesac_F(x1,y1,x2,y2, n_matches, m3, no_samp, f_threshold)
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26 | %as in ransac and torr mlesac/mapsac papers
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27 |
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28 |
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29 | case {'mapsac',1}
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30 | if isempty(f_optim_parameters)
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31 | no_samp =1000;
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32 | T = 4;
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33 | else
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34 | no_samp = f_optim_parameters(1);
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35 | T = f_optim_parameters(2);
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36 | end
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37 | [f,f_sq_errors, n_inliers,inlier_index] = torr_mapsac_F(x1,y1,x2,y2, no_matches, m3, no_samp, T);
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38 |
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39 |
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40 | case {'linear',2}
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41 | f = torr_estf(x1,y1,x2,y2, no_matches,m3);
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42 |
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43 | %as in Torr & Fitzgibbon paper
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44 | case {'bookstein',3}
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45 | f = torr_estf_bookstein(x1,y1,x2,y2, no_matches,m3);
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46 |
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47 |
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48 |
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49 | case {'b+sampson','boosam',4}
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50 | f = torr_estf_bookstein_sampson(x1,y1,x2,y2, no_matches,m3);
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51 |
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52 | case {'non_linear',5}
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53 | f = torr_nonlinf_mincon2x2(f_init, x1,y1,x2,y2, no_matches, m3);
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54 |
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55 | case {'lin+non_lin',6}
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56 | set_rank2 = 1;
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57 | f = torr_estimateF(matches, m3, [], 'linear',set_rank2);
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58 | f = torr_estimateF(matches, m3, [], 'non_linear',set_rank2,f);
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59 |
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60 | case {'hegel1',7}
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61 | f = torr_estimateF(matches, m3, [], 'linear');
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62 | f = ant_fnsf(x1,y1,x2,y2,m3,f);
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63 |
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64 | otherwise
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65 | disp('Unknown method.')
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66 | end
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67 |
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68 |
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69 |
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70 | F = reshape(f, 3, 3);
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71 | %make it my way round
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72 | F = F';
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73 |
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74 |
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75 |
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76 | %the F matrix is defined like:
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77 | % (nx2, ny2, m3) f(1 2 3) nx1
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78 | % (4 5 6) ny1
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79 | % (7 8 9) m3
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80 |
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81 | % disp('before rank 2')
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82 | % F
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83 | if set_rank2
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84 | [U,S,V] = svd(F);
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85 | S(3,3) = 0;
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86 | F = U*S*V';
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87 | f = reshape(F',9,1);
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88 | end
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89 | % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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90 | % disp('after rank 2')
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91 | % F
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92 |
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93 | % % Unit fro-norm F:
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94 | % fn = norm(F(:));
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95 | % fn = 2^(-floor(log(fn) / log(2)));
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96 | % F = F * fn;
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97 |
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98 | F = F/norm(F,'fro');
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99 | f = f/norm(f);
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100 |
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101 | switch lower(method)
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102 | case {'bookstein',3,'linear',2,'bookstein',3,'b+sampson','boosam',4,'non_linear',5,'lin+non_lin',6, ...
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103 | 'hegel1',7}
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104 | %f = reshape(F',9,1); %calculate squared errors (distance to manifold of F)
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105 | f_sq_errors = torr_errf2(f,x1,y1,x2,y2, no_matches, m3);
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106 | %next generate index set of inliers
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107 | T = 10;
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108 | inlier_index = find((f_sq_errors < T) == 1);
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109 | n_inliers = length(inlier_index);
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110 |
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111 |
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112 | end
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113 |
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