From f618466c25d43f3bae9e40920273bf77de1e1149 Mon Sep 17 00:00:00 2001
From: leochanj105 <leochanj@live.unc.edu>
Date: Mon, 19 Oct 2020 23:09:30 -0400
Subject: initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs
---
 .../toolbox/toolbox_basic/affine/compute_AD.m      | 90 ++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100755 SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m

(limited to 'SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m')

diff --git a/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m
new file mode 100755
index 0000000..a39acd6
--- /dev/null
+++ b/SD-VBS/common/toolbox/toolbox_basic/affine/compute_AD.m
@@ -0,0 +1,90 @@
+function [A,D,mask] =...
+compute_AD(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,num_trans,Dest,mask)
+%
+% function [A,D,mask] = ...
+%     compute_AD(img_i,img_j,center_i,center_j,window_size_h,num_iter,w,
+%                mask,num_trans)
+%
+%  A: 	 Affine motion;
+%  D: 	 Displacement;
+%  
+%  img_i, img_j: the two image(in full size);
+%  center_i, center_j:  the centers of the feature in two images;
+%  window_size_h: half size of the feature window;
+%  num_iter: 	number of iterations;
+%  w: 		parameter used in "grad.m" for computing gaussians used for
+%      		gradient estimation;
+%
+%  num_trans:   OPTIONAL, number of translation iteration; default = 3;
+%  mask:  	OPTIONAL, if some area of the square shaped feature window should
+%         		  be weighted less;
+%
+
+%
+%    Jianbo Shi
+%
+
+if ~exist('Dest'),
+  Dest = [0,0];
+end
+
+if ~exist('mask'),
+   mask = ones(2*window_size_h+1)';
+end
+
+% set the default num_trans
+if ~exist('num_trans'),
+   num_trans= 3;
+end
+
+% normalize image intensity to the range of 0.0-1.0
+img_i = norm_inten(img_i);
+img_j = norm_inten(img_j);
+
+window_size = 2*window_size_h + 1;
+I = carve_it(img_i,center_i,window_size_h);
+J = carve_it(img_j,center_j,window_size_h);
+
+% init. step
+J_computed = I;
+D_computed = Dest;
+A_computed = eye(2);
+J_computed = compute_J(A_computed,D_computed,img_i,center_i,window_size_h);
+
+%% level of noise
+sig = 0.1;
+
+records = zeros(num_iter,6);
+errs = zeros(1,num_iter);
+
+k = 1;
+% iteration
+while k <= num_iter,
+  [A,D] = iter_AD(J_computed,J,mask,w,k,num_trans);
+
+  A_computed = A*A_computed;
+  D_computed = (A*D_computed')' + D;
+
+  % compute the warped image
+  J_computed = compute_J(A_computed,D_computed,img_i,center_i,window_size_h);
+
+  % compute the SSD error
+  errs(k) = sqrt(sum(sum((mask.*(J_computed-J)).^2)))/prod(size(J));
+
+  % update the mask, discounting possible occlusion region
+  if (k>num_trans),
+    mask = exp(-abs(J_computed-J)/sig);
+  end
+
+  % record the A and D
+  records(k,:) = [reshape(A_computed,1,4),reshape(D_computed,1,2)];
+
+  k = k+1;
+end  
+
+[tmp,id] = min(errs);
+A = reshape(records(id,1:4),2,2);
+D = reshape(records(id,5:6),1,2);
+
+J_computed = compute_J(A,D,img_i,center_i,window_size_h);
+mask = exp(-abs(J_computed-J)/sig);
-- 
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