initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

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 @@
+function [NcutDiscretes,eigenVectors,eigenValues] = MNcut(I,nsegs);
+%
+%  [NcutDiscrete,eigenVectors,eigenValues] = MNcut(I,nsegs);
+%  
+%
+
+[nr,nc,nb] = size(I);
+
+max_image_size = max(nr,nc);
+
+% modified by song, 06/13/2005
+%   test parameters
+if (1) % original settings
+    if (max_image_size>120) & (max_image_size<=500),
+        % use 3 levels,
+        data.layers.number=3;
+        data.layers.dist=3;
+        data.layers.weight=[3000,4000,10000];
+        data.W.scales=[1,2,3];%[1,2,3];
+        data.W.radius=[2,3,7];%[2,3,7];
+    elseif (max_image_size >500),
+        % use 4 levels,
+        data.layers.number=4;
+        data.layers.dist=3;
+        data.layers.weight=[3000,4000,10000,20000];
+        data.W.scales=[1,2,3,3];
+        data.W.radius=[2,3,4,6];
+    elseif (max_image_size <=120)
+        data.layers.number=2;
+        data.layers.dist=3;
+        data.layers.weight=[3000,10000];
+        data.W.scales=[1,2];
+        data.W.radius=[2,6];
+    end
+else % test setting
+    if (max_image_size>200) & (max_image_size<=500),
+        % use 3 levels,
+        data.layers.number=3;
+        data.layers.dist=3;
+        data.layers.weight=[3000,4000,10000];
+        data.W.scales=[1,2,3];%[1,2,3];
+        data.W.radius=[2,3,7];%[2,3,7];
+    elseif (max_image_size >500),
+        % use 4 levels,
+        data.layers.number=4;
+        data.layers.dist=3;
+        data.layers.weight=[3000,4000,10000,20000];
+        data.W.scales=[1,2,3,3];
+        data.W.radius=[2,3,4,6];
+    elseif (max_image_size <=200)
+        data.layers.number=2;
+        data.layers.dist=3;
+        data.layers.weight=[3000,10000];
+        data.W.scales=[1,2];
+        data.W.radius=[2,4];
+    end
+
+end;
+
+
+data.W.edgeVariance=0.1; %0.1
+data.W.gridtype='square';
+data.W.sigmaI=0.12;%0.12
+data.W.sigmaX=1000;
+data.W.mode='mixed';    
+data.W.p=0;
+data.W.q=0;
+
+%eigensolver
+data.dataGraphCut.offset = 100;% 10; %valeur sur diagonale de W (mieux vaut 10 pour valeurs negatives de W)
+data.dataGraphCut.maxiterations=50;% voir
+data.dataGraphCut.eigsErrorTolerance=1e-2;%1e-6;
+data.dataGraphCut.valeurMin=1e-6;%1e-5;% utilise pour tronquer des valeurs et sparsifier des matrices
+data.dataGraphCut.verbose = 0;
+
+data.dataGraphCut.nbEigenValues=max(nsegs);
+
+disp('computeEdge');
+[multiWpp,ConstraintMat, Wind,data,emag,ephase]= computeMultiW (I,data);
+
+disp('Ncut');
+[eigenVectors,eigenValues]= eigSolve (multiWpp,ConstraintMat,data);
+
+%NcutDiscretes = zeros(nr,nc,length(nsegs));
+NcutDiscretes = zeros(nr,nc,(nsegs));
+
+for j=1:length(nsegs),
+        nseg = nsegs(j);
+        [nr,nc,nb] = size(eigenVectors(:,:,1:nseg));
+        [NcutDiscrete,evrotated] =discretisation(reshape(eigenVectors(:,:,1:nb),nr*nc,nb),nr,nc);
+         NcutDiscretes(:,:,j) = NcutDiscrete;
+end