[37] | 1 | % By Philip Torr 2002
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| 2 | % copyright Microsoft Corp.
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| 3 | %
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| 4 | % %designed for the good of the world by Philip Torr
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| 5 | % copyright Philip Torr and Microsoft Corp 2002
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| 6 | % linear estimation of H
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| 7 | %
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| 8 | % @article{Torr99c,
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| 9 | % author = "Torr, P. H. S. and Zisserman, A",
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| 10 | % title ="MLESAC: A New Robust Estimator with Application to Estimating Image Geometry ",
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| 11 | % journal = "CVIU",
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| 12 | % Volume = {78},
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| 13 | % number = 1,
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| 14 | % pages = {138-156},
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| 15 | % year = 2000}
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| 16 | %
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| 17 | % %MAPSAC is the Bayesian version of MLESAC, and it is easier to pronounce!
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| 18 | % it is described in:
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| 19 | %
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| 20 | % @article{Torr02d,
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| 21 | % author = "Torr, P. H. S.",
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| 22 | % title ="Bayesian Model Estimation and Selection for Epipolar Geometry and
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| 23 | % Generic Manifold Fitting",
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| 24 | % journal = "IJCV",
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| 25 | % Volume = {?},
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| 26 | % number = ?,
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| 27 | % pages = {?},
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| 28 | % url = "http://research.microsoft.com/~philtorr/",
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| 29 | % year = 2002}
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| 30 | %
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| 31 |
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| 32 | function [h,h_sq_errors, n_inliers,inlier_index] = torr_napsac_H(x1,y1,x2,y2, no_matches,m3, no_samp, T)
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| 33 |
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| 34 | %disp('mapsac-ing H')
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| 35 | %bestsse = T * no_matches + 1;
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| 36 |
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| 37 | %%%%%%%%%%debug
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| 38 | %used for debugging:
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| 39 | no_trials = 1;
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| 40 | max_inliers = 0;
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| 41 | %%%%%%%%%%end debug
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| 42 |
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| 43 |
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| 44 |
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| 45 | for(i = 1:1)
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| 46 | %for(i = 1:no_samp)
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| 47 |
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| 48 | choice = randperm(no_matches);
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| 49 | %NAPSAC frenzyoid! first pick one point then take 6 nearest, described in thesis/china paper
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| 50 | distance_xyxy = (x1 - x1(choice(1))).^2 + (x2 - x2(choice(1))).^2 + (y1 - y1(choice(1))).^2 + (y2 - y2(choice(1))).^2;
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| 51 | [sorted_distance_xyxy, index_distance_xyxy] = sort(distance_xyxy);
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| 52 |
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| 53 | %next randomly permute the best 50 matches
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| 54 | choice2 = randperm(60);
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| 55 |
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| 56 | for (j = 1:8)
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| 57 | tx1(j) = x1( index_distance_xyxy(choice2(j)));
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| 58 | tx2(j) = x2( index_distance_xyxy(choice2(j)));
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| 59 | ty1(j) = y1( index_distance_xyxy(choice2(j)));
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| 60 | ty2(j) = y2( index_distance_xyxy(choice2(j)));
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| 61 | end % for (j = 1:7)
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| 62 |
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| 63 | % tx1 = x1( index_distance_xyxy(1:7));
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| 64 | % tx2 = x2( index_distance_xyxy(1:7));
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| 65 | % ty1 = y1( index_distance_xyxy(1:7));
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| 66 | % ty2 = y2( index_distance_xyxy(1:7));
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| 67 | %set up local design matrix
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 | figure
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| 81 | %take minimum of matches; minc provides match scores
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| 82 | title('First Image: plus matches')
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| 83 | hold on
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| 84 |
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| 85 | for j = 1:5
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| 86 | a = [x1( index_distance_xyxy(choice2(j))),x2( index_distance_xyxy(choice2(j)))]; %x1 x2
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| 87 | b = [y1( index_distance_xyxy(choice2(j))),y2( index_distance_xyxy(choice2(j)))]; %y1 y2
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| 88 | %x1 y1
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| 89 | %x2 y2
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| 90 | line(a,b);
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| 91 | end
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| 92 | hold off
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| 93 |
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| 94 | %matches = mat12;
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 | for (j = 1:4)
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| 102 | tx1(j) = x1( choice(j));
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| 103 | tx2(j) = x2( choice(j));
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| 104 | ty1(j) = y1( choice(j));
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| 105 | ty2(j) = y2( choice(j));
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| 106 |
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| 107 | end
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| 108 |
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| 109 | %generate trial h
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| 110 | ht = torr_esth(tx1,ty1,tx2,ty2,4,m3);
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| 111 |
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| 112 | %get squared errors
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| 113 | et = torr_errh(ht,x1,y1,x2,y2, no_matches, m3);
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| 114 |
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| 115 | %capped residuals
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| 116 | cet = min(et,T);
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| 117 | sse = cet' * cet;
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| 118 |
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| 119 |
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| 120 | if i ==1
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| 121 | h = ht;
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| 122 | bestsse = sse;
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| 123 | elseif bestsse > sse
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| 124 | h = ht;
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| 125 | bestsse = sse;
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| 126 | end
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| 127 |
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| 128 | %monitor progress %debug
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| 129 | inlier_index = find((et < T) == 1);
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| 130 | mapsac_inliers(no_trials) = length(inlier_index);
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| 131 | if mapsac_inliers(no_trials) > max_inliers
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| 132 | max_inliers = mapsac_inliers(no_trials);
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| 133 | else
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| 134 | mapsac_inliers(no_trials) = max_inliers;
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| 135 | end
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| 136 | no_trials = no_trials + 1;
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| 137 | %%%%%%%%end debug
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| 138 |
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| 139 |
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| 140 | end
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| 141 | %calculate squared errors (distance to manifold of F)
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| 142 | h_sq_errors = torr_errh(h,x1,y1,x2,y2, no_matches, m3);
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| 143 | %next generate index set of inliers
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| 144 | inlier_index = find((h_sq_errors < T) == 1);
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| 145 | n_inliers = length(inlier_index);
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 | %%%%%%%%%%debug
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| 152 | %for NAPSAC paper
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| 153 | no_matches
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| 154 | n_inliers
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| 155 | no_trials
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| 156 |
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| 157 | mapsac_inliers(1:30)
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| 158 | %find out how many it took to get to n_inliers
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| 159 | perc = n_inliers;
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| 160 | map_index = find((mapsac_inliers < perc) == 1);
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| 161 | perc100 = length(map_index)+1
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| 162 | %find out how many it took to get to n_inliers
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| 163 |
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| 164 | perc = n_inliers * 0.9;
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| 165 | map_index = find((mapsac_inliers < perc) == 1);
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| 166 | perc90 = length(map_index)+1
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| 167 |
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| 168 | perc = n_inliers * 0.8;
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| 169 | map_index = find((mapsac_inliers < perc) == 1);
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| 170 | perc80 = length(map_index)+1
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| 171 |
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| 172 |
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| 173 |
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| 174 | perc = n_inliers * 0.7;
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| 175 | map_index = find((mapsac_inliers < perc) == 1);
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| 176 | perc70 = length(map_index)+1
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| 177 |
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| 178 |
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| 179 |
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| 180 | perc = n_inliers * 0.6;
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| 181 | map_index = find((mapsac_inliers < perc) == 1);
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| 182 | perc60 = length(map_index)+1
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| 183 |
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| 184 | n_inliers
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| 185 |
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| 186 | disp('Napsac');
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