initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

3 files changed, 80 insertions, 0 deletions

diff --git a/SD-VBS/common/toolbox/ikkjin/getANMS.m b/SD-VBS/common/toolbox/ikkjin/getANMS.m
new file mode 100755
index 0000000..a40d50c
--- /dev/null
+++ b/SD-VBS/common/toolbox/ikkjin/getANMS.m
@@ -0,0 +1,30 @@
+function [interestPnts]=getANMS(x, y, v, r, dataDir)
+MAX_LIMIT=100000;
+C_ROBUST=1;
+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);
+     
+    iter = iter + 1;
+    supId=find(suppressR>r_sq);
+end
diff --git a/SD-VBS/common/toolbox/ikkjin/getImgGrad.m b/SD-VBS/common/toolbox/ikkjin/getImgGrad.m
new file mode 100755
index 0000000..90fae28
--- /dev/null
+++ b/SD-VBS/common/toolbox/ikkjin/getImgGrad.m
@@ -0,0 +1,7 @@
+function Ig=getImgGrad(imgroi)
+im = double(rgb2gray(imgroi));
+g1 = fspecial('gaussian', 9,1);  % Gaussian with sigma_d
+img1 = conv2(im,g1,'same');  % blur image with sigma_d
+Ix = conv2(img1,[-1 0 1],'same');  % take x derivative
+Iy = conv2(img1,[-1;0;1],'same');  % take y derivative
+Ig=Ix.^2+Iy.^2;
\ No newline at end of file
diff --git a/SD-VBS/common/toolbox/ikkjin/harris.m b/SD-VBS/common/toolbox/ikkjin/harris.m
new file mode 100755
index 0000000..92a6543
--- /dev/null
+++ b/SD-VBS/common/toolbox/ikkjin/harris.m
@@ -0,0 +1,43 @@
+
+% Sample code for detecting Harris corners, following 
+% Brown et al, CVPR 2005
+%        by Alyosha Efros, so probably buggy...
+%        slightly modified by ikkjin
+
+function [x,y,v] = harris(imrgb);
+[nr nc nb]=size(imrgb);
+if nb==3
+    im=rgb2gray(imrgb);
+else
+    im=imrgb;
+end
+
+im = im2double(im);
+g1 = fspecial('gaussian', 9,1);  % Gaussian with sigma_d 
+g2 = fspecial('gaussian', 11,1.5); % Gaussian with sigma_i 
+img1 = conv2(im,g1,'same');  % blur image with sigma_d
+Ix = conv2(img1,[-1 0 1],'same');  % take x derivative 
+Iy = conv2(img1,[-1;0;1],'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
+
+% don't want corners close to image border
+R([1:15, end-16:end], :) = 0;
+R(:,[1:15,end-16:end]) = 0;
+
+% non-maxima supression within 3x3 windows
+nonmax = inline('max(x)');
+Rmax = colfilt(R,[3 3],'sliding',nonmax); % find neighbrhood max
+Rnm = R.*(R == Rmax);  % supress non-max
+
+% extract all interest points
+[y,x,v] = find(Rnm);
+
+
+