16 files changed, 0 insertions, 325 deletions

diff --git a/SD-VBS/benchmarks/stitch/src/matlab/calculateH.m b/SD-VBS/benchmarks/stitch/src/matlab/calculateH.m
deleted file mode 100755
index caefd74..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/calculateH.m
+++ /dev/null
@@ -1,24 +0,0 @@
-function retH=calculateH(pnt1, pnt2)
-
-[n temp]=size(pnt1);
-
-X=zeros(0,8);
-b=zeros(0,1);
-
-for i=1:n
-    X=[X; pnt1(i,1) pnt1(i,2) 1 0 0 0 -pnt2(i,1)*pnt1(i,1) -pnt2(i,1)*pnt1(i,2);0, 0, 0, pnt1(i,1), pnt1(i,2), 1, -pnt2(i,2)*pnt1(i,1), -pnt2(i,2)*pnt1(i,2)];
-    b=[b; pnt2(i,1); pnt2(i,2)];
-end
-
-if n==4
-    %if abs(det(X)~=0
-        H=X^(-1)*b;
-    %else
-    %    H=[0 0 0; 0 0 0; 0 0 1];
-    %end
-else
-    Xt = X';
-    H=(Xt*X)^(-1)*Xt*b;
-end
-
-retH=[H(1) H(2) H(3);H(4) H(5) H(6);H(7) H(8) 1];
diff --git a/SD-VBS/benchmarks/stitch/src/matlab/dist2.m b/SD-VBS/benchmarks/stitch/src/matlab/dist2.m
deleted file mode 100755
index 89116ac..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/dist2.m
+++ /dev/null
@@ -1,31 +0,0 @@
-function n2 = dist2(x, c)
-%DIST2  Calculates squared distance between two sets of points.
-%
-%       Description
-%       D = DIST2(X, C) takes two matrices of vectors and calculates the
-%       squared Euclidean distance between them.  Both matrices must be of
-%       the same column dimension.  If X has M rows and N columns, and C has
-%       L rows and N columns, then the result has M rows and L columns.  The
-%       I, Jth entry is the  squared distance from the Ith row of X to the
-%       Jth row of C.
-%
-%       See also
-%       GMMACTIV, KMEANS, RBFFWD
-%
-
-%       Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
-
-[ndata, dimx] = size(x);
-[ncentres, dimc] = size(c);
-if dimx ~= dimc
-        error('Data dimension does not match dimension of centres')
-end
-
-ct = c';
-opt = (x.^2)';
-op2t = (x.^2)';
-tempT = (ones(ncentres, 1) * sum(opt, 1))';
-n2 = tempT + ones(ndata, 1) * sum(op2t,1) - 2.*(x*ct);
-
-
-
diff --git a/SD-VBS/benchmarks/stitch/src/matlab/extractFeatures.m b/SD-VBS/benchmarks/stitch/src/matlab/extractFeatures.m
deleted file mode 100755
index 38d2f7c..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/extractFeatures.m
+++ /dev/null
@@ -1,56 +0,0 @@
-function vecF=extractFeatures(I, x, y, dataDir)
-
-Igray = I;
-w = (1/16)*[1 4 6 4 1];
-[n temp]=size(x);
-
-%SUB sample rate s=5
-wt = w';
-Iconv=conv2(w, wt, Igray, 'same');
-Iconv=conv2(w, wt, Iconv, 'same');
-Isub=Iconv(1:5:end,1:5:end);
-[nr nc]=size(Isub);
-    
-Xsub=min(floor(x/5)+1, nc-4);
-Ysub=min(floor(y/5)+1, nr-4);
-
-if(length(Xsub) < 6 || length(Ysub) < 10)
-    vecF = [Xsub Ysub];
-    return;
-end
-
-newSize = 4;
-[rows, cols] = size(I);
-if(rows > 32 & cols >32)
-    newSize = 64;
-end
-
-for i=1:n
-
-    minValy = 3;
-    minValx = 3;
-    if( Ysub(i) < 4)
-        Ysub(i) = 4;
-    end
-    if( Xsub(i) < 4)
-        Xsub(i) = 4;
-    end
-
-    if( Ysub(i) > size(Isub,1)-4)
-        Ysub(i) = size(Isub,1)-4;
-    end
-    if( Xsub(i) > size(Isub,2)-4)
-        Xsub(i) = size(Isub,2)-4;
-    end
-    
-    vecF(i,:)=reshape(Isub(Ysub(i)-minValy:Ysub(i)+4,Xsub(i)-minValx:Xsub(i)+4),1,newSize);
-
-    %normalization
-    vecF(i,:)=vecF(i,:)-mean(vecF(i,:));
-    vecF(i,:)=vecF(i,:)/std(vecF(i,:));
-    %imagesc(reshape(vecF(i,:),8,8)); colormap gray
-    %drawnow
-    %pause    
-end
-
-
diff --git a/SD-VBS/benchmarks/stitch/src/matlab/getANMS.m b/SD-VBS/benchmarks/stitch/src/matlab/getANMS.m
deleted file mode 100755
index caece31..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/getANMS.m
+++ /dev/null
@@ -1,25 +0,0 @@
-function [interestPnts]=getANMS(x, y, v, r, dataDir)
-%interestPnts=[x y v]
-MAX_LIMIT=100000000;
-C_ROBUST=0.9;
-r_sq=r^2;
-points=[x y v];
-[n temp]=size(v);
-[srtdV srtdVIdx]=sort(v,'descend');
-srtdPnts=points(srtdVIdx,:);
-
-interestPnts=zeros(0,3);
-
-suppressR=ones(n,1)*MAX_LIMIT;
-supId=find(suppressR>r_sq);
-
-iter = 0;
-while length(supId)>0
-    interestPnts=[interestPnts; srtdPnts(supId(1),:)];
-    srtdPnts=srtdPnts(supId(2:end),:);
-    suppressR=suppressR(supId(2:end),:);
-    
-    suppressR=min(suppressR,(C_ROBUST*interestPnts(end,3)>srtdPnts(:,3)).*((srtdPnts(:,1)-interestPnts(end,1)).^2 + (srtdPnts(:,2)-interestPnts(end,2)).^2)+(C_ROBUST*interestPnts(end,3)<=srtdPnts(:,3))*MAX_LIMIT);
-    supId=find(suppressR>r_sq);
-    iter = iter + 1;
-end
diff --git a/SD-VBS/benchmarks/stitch/src/matlab/getDirParamLocal.m b/SD-VBS/benchmarks/stitch/src/matlab/getDirParamLocal.m
deleted file mode 100755
index 7b36886..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/getDirParamLocal.m
+++ /dev/null
@@ -1,4 +0,0 @@
-function DIR_PARAM=getDirParam
-DIR_PARAM.main_dir=pwd;
-DIR_PARAM.data_dir=fullfile(DIR_PARAM.main_dir,'data');
-DIR_PARAM.report_dir=fullfile(DIR_PARAM.main_dir,'report');
diff --git a/SD-VBS/benchmarks/stitch/src/matlab/harris.m b/SD-VBS/benchmarks/stitch/src/matlab/harris.m
deleted file mode 100755
index 423330e..0000000
--- a/SD-VBS/benchmarks/stitch/src/matlab/harris.m
+++ /dev/null
@@ -1,37 +0,0 @@
-
-% Sample code for detecting Harris corners, following 
-% Brown et al, CVPR 2005
-%        by Alyosha Efros, so probably buggy...
-
-function [x,y,v] = harris(im, dataDir);
-
-g1 = [1,4,6,4,1; 4,16,24,16,4;6,24,36,24,6;4,16,24,16,4;1,4,6,4,1]/256;
-g2 = [1,2,1;2,4,2;1,2,1]/16;
-
-img1 = conv2(im,g1,'same');  % blur image with sigma_d
-     
-Ix = conv2(img1,[-0.5 0 0.5],'same');  % take x derivative 
-Iy = conv2(img1,[-0.5;0;0.5],'same');  % take y derivative
-
-% Compute elements of the Harris matrix H
-%%% we can use blur instead of the summing window
-Ix2 = conv2(Ix.*Ix,g2,'same');
-Iy2 = conv2(Iy.*Iy,g2,'same');
-IxIy = conv2(Ix.*Iy,g2,'same'); 
-
-R = (Ix2.*Iy2 - IxIy.*IxIy) ... % det(H) 
-    ./ (Ix2 + Iy2 + eps);       % trace(H) + epsilon