[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 based on ideas contained in
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| 5 | % copyright Philip Torr and Microsoft Corp 2002
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| 6 | %
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| 7 |
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| 8 |
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| 9 | % /*
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| 10 | %
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| 11 | % @inproceedings{Torr93b,
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| 12 | % author = "Torr, P. H. S. and Murray, D. W.",
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| 13 | % title = "Outlier Detection and Motion Segmentation",
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| 14 | % booktitle = "Sensor Fusion VI",
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| 15 | % editor = "Schenker, P. S.",
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| 16 | % publisher = "SPIE volume 2059",
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| 17 | % note = "Boston",
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| 18 | % pages = {432-443},
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| 19 | % year = 1993 }
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| 20 | %
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| 21 | %
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| 22 | % @phdthesis{Torr:thesis,
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| 23 | % author="Torr, P. H. S.",
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| 24 | % title="Outlier Detection and Motion Segmentation",
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| 25 | % school=" Dept. of Engineering Science, University of Oxford",
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| 26 | % year=1995}
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| 27 | %
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| 28 | %
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| 29 | %
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| 30 | % @article{Torr97c,
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| 31 | % author="Torr, P. H. S. and Murray, D. W. ",
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| 32 | % title="The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix",
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| 33 | % journal="IJCV",
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| 34 | % volume = 24,
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| 35 | % number = 3,
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| 36 | % pages = {271--300},
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| 37 | % year=1997
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| 38 | % }
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| 39 | %
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| 40 | %
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| 41 | %
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| 42 | %
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| 43 | % @article{Torr99c,
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| 44 | % author = "Torr, P. H. S. and Zisserman, A",
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| 45 | % title ="MLESAC: A New Robust Estimator with Application to Estimating Image Geometry ",
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| 46 | % journal = "CVIU",
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| 47 | % Volume = {78},
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| 48 | % number = 1,
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| 49 | % pages = {138-156},
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| 50 | % year = 2000}
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| 51 |
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| 52 | %threshold is the maximum squared value of the residuals
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| 53 | %no_matches is the number of matches
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| 54 | %no_samp is the number of random samples to be taken
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| 55 | %m3 is the estimate of the 3rf projective coordinate (f in pixels)
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| 56 |
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| 57 | %the F matrix is defined like:
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| 58 | % (nx2, ny2, m3) f(1 2 3) nx1
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| 59 | % (4 5 6) ny1
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| 60 | % (7 8 9) m3
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| 61 |
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| 62 |
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| 63 |
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| 64 | %we minimize a robust function min(e^2,T) see MLESAC paper.
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| 65 |
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| 66 |
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| 67 | function f = torr_mlesac_F(x1,y1,x2,y2, no_matches, m3, no_samp, T)
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| 68 | %disp('This just does calculation of perfect data,for test')
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| 69 | %disp('Use estf otherwise')
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| 70 | %f = rand(9);
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| 71 |
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| 72 |
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| 73 | for(i = 1:no_samp)
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| 74 | choice = randperm(no_matches);
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| 75 | %set up local design matrix, here we estimate from 7 matches
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| 76 | for (j = 1:7)
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| 77 | tx1(j) = x1( choice(j));
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| 78 | tx2(j) = x2( choice(j));
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| 79 | ty1(j) = y1( choice(j));
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| 80 | ty2(j) = y2( choice(j));
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| 81 | end % for (j = 1:7)
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| 82 |
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| 83 |
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| 84 | %produces 1 or 3 solutions.
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| 85 | [no_F big_result]= torr_F_constrained_fit(tx1,ty1,tx2,ty2,m3);
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| 86 |
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| 87 | for j = 1:no_F
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| 88 | ft = big_result(j,:);
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| 89 |
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| 90 | %get squared errors
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| 91 | et = torr_errf2(ft,x1,y1,x2,y2, no_matches, m3);
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| 92 | %capped residuals
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| 93 | cet = min(et,T);
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| 94 | sse = cet' * cet;
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| 95 | % use sse 0 to bring it to a reasonable value
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| 96 | if ((i ==1) & (j ==1))
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| 97 | f = ft;
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| 98 | bestsse = sse;
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| 99 | elseif bestsse > sse
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| 100 | f = ft;
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| 101 | bestsse = sse;
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| 102 | end
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| 103 |
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| 104 | end
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| 105 |
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| 106 | end %for(i = 1:no_samp)
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| 107 |
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| 108 |
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| 109 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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| 110 |
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