initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

1 files changed, 37 insertions, 0 deletions

diff --git a/SD-VBS/benchmarks/stitch/src/matlab/harris.m b/SD-VBS/benchmarks/stitch/src/matlab/harris.m
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+
+% 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
+
+% don't want corners close to image border
+[rows, cols] = size(im);
+
+% 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);
+
+
+