diff options
Diffstat (limited to 'SD-VBS/common/toolbox/MultiNcut')
75 files changed, 4657 insertions, 0 deletions
diff --git a/SD-VBS/common/toolbox/MultiNcut/MNcut.m b/SD-VBS/common/toolbox/MultiNcut/MNcut.m new file mode 100755 index 0000000..5486080 --- /dev/null +++ b/SD-VBS/common/toolbox/MultiNcut/MNcut.m | |||
| @@ -0,0 +1,93 @@ | |||
| 1 | function [NcutDiscretes,eigenVectors,eigenValues] = MNcut(I,nsegs); | ||
| 2 | % | ||
| 3 | % [NcutDiscrete,eigenVectors,eigenValues] = MNcut(I,nsegs); | ||
| 4 | % | ||
| 5 | % | ||
| 6 | |||
| 7 | [nr,nc,nb] = size(I); | ||
| 8 | |||
| 9 | max_image_size = max(nr,nc); | ||
| 10 | |||
| 11 | % modified by song, 06/13/2005 | ||
| 12 | % test parameters | ||
| 13 | if (1) % original settings | ||
| 14 | if (max_image_size>120) & (max_image_size<=500), | ||
| 15 | % use 3 levels, | ||
| 16 | data.layers.number=3; | ||
| 17 | data.layers.dist=3; | ||
| 18 | data.layers.weight=[3000,4000,10000]; | ||
| 19 | data.W.scales=[1,2,3];%[1,2,3]; | ||
| 20 | data.W.radius=[2,3,7];%[2,3,7]; | ||
| 21 | elseif (max_image_size >500), | ||
| 22 | % use 4 levels, | ||
| 23 | data.layers.number=4; | ||
| 24 | data.layers.dist=3; | ||
| 25 | data.layers.weight=[3000,4000,10000,20000]; | ||
| 26 | data.W.scales=[1,2,3,3]; | ||
| 27 | data.W.radius=[2,3,4,6]; | ||
| 28 | elseif (max_image_size <=120) | ||
| 29 | data.layers.number=2; | ||
| 30 | data.layers.dist=3; | ||
| 31 | data.layers.weight=[3000,10000]; | ||
| 32 | data.W.scales=[1,2]; | ||
| 33 | data.W.radius=[2,6]; | ||
| 34 | end | ||
| 35 | else % test setting | ||
| 36 | if (max_image_size>200) & (max_image_size<=500), | ||
| 37 | % use 3 levels, | ||
| 38 | data.layers.number=3; | ||
| 39 | data.layers.dist=3; | ||
| 40 | data.layers.weight=[3000,4000,10000]; | ||
| 41 | data.W.scales=[1,2,3];%[1,2,3]; | ||
| 42 | data.W.radius=[2,3,7];%[2,3,7]; | ||
| 43 | elseif (max_image_size >500), | ||
| 44 | % use 4 levels, | ||
| 45 | data.layers.number=4; | ||
| 46 | data.layers.dist=3; | ||
| 47 | data.layers.weight=[3000,4000,10000,20000]; | ||
| 48 | data.W.scales=[1,2,3,3]; | ||
| 49 | data.W.radius=[2,3,4,6]; | ||
| 50 | elseif (max_image_size <=200) | ||
| 51 | data.layers.number=2; | ||
| 52 | data.layers.dist=3; | ||
| 53 | data.layers.weight=[3000,10000]; | ||
| 54 | data.W.scales=[1,2]; | ||
| 55 | data.W.radius=[2,4]; | ||
| 56 | end | ||
| 57 | |||
| 58 | end; | ||
| 59 | |||
| 60 | |||
| 61 | data.W.edgeVariance=0.1; %0.1 | ||
| 62 | data.W.gridtype='square'; | ||
| 63 | data.W.sigmaI=0.12;%0.12 | ||
| 64 | data.W.sigmaX=1000; | ||
| 65 | data.W.mode='mixed'; | ||
| 66 | data.W.p=0; | ||
| 67 | data.W.q=0; | ||
| 68 | |||
| 69 | %eigensolver | ||
| 70 | data.dataGraphCut.offset = 100;% 10; %valeur sur diagonale de W (mieux vaut 10 pour valeurs negatives de W) | ||
| 71 | data.dataGraphCut.maxiterations=50;% voir | ||
| 72 | data.dataGraphCut.eigsErrorTolerance=1e-2;%1e-6; | ||
| 73 | data.dataGraphCut.valeurMin=1e-6;%1e-5;% utilise pour tronquer des valeurs et sparsifier des matrices | ||
| 74 | data.dataGraphCut.verbose = 0; | ||
| 75 | |||
| 76 | data.dataGraphCut.nbEigenValues=max(nsegs); | ||
| 77 | |||
| 78 | disp('computeEdge'); | ||
| 79 | [multiWpp,ConstraintMat, Wind,data,emag,ephase]= computeMultiW (I,data); | ||
| 80 | |||
| 81 | disp('Ncut'); | ||
| 82 | [eigenVectors,eigenValues]= eigSolve (multiWpp,ConstraintMat,data); | ||
| 83 | |||
