broke the normalization process

BenFiles/computeH.m

@@ -5,10 +5,7 @@
 % p1 and p2 are 2xN matrices of corresponding (x,y)' coordinates between
 % two images
 
-    % pad p1 and p2 with 1s for scale factor
     N = length(p1);
-    p1 = [p1; ones(1, N)];
-    p2 = [p2; ones(1, N)];
 
     A = zeros(2*N, 9);
     zerot = [0 0 0];

BenFiles/computeH_norm.m

@@ -2,15 +2,46 @@
 % Section 4.2: Implementation of normalized planar homography in 2D.
 
 
-function [ H2to1 ] = computeH_norm( p1, p2)
+function [ sim, H2to1 ] = computeH_norm( p1, p2)
 % Normalize the coordinates p1 and p2 prior to calling compute_H. 
 
 % p1 and p2 are 2xN matrices of corresponding (x,y)' coordinates between
 % two images
 
+% Translate the centroid of the points to the origin.
+mux = mean(p1(1,:));
+muy = mean(p1(2,:));
+transx = p1(1,:) - mux;
+transy = p1(2,:) - muy;
+
+% Scale the points so that the average distance from the origin is sqrt(2).
+avgDist = mean(sqrt(transx.^2 + transy.^2));
+scale = sqrt(2)/avgDist;
+
+sim = [scale 0 -scale*mux;
+       0 scale -scale*muy;
+       0 0 1];
+
+normP2 = sim*p1;
+
+% Translate the centroid of the points to the origin.
+mux = mean(p2(1,:));
+muy = mean(p2(2,:));
+transx = p1(1,:) - mux;
+transy = p1(2,:) - muy;
+
+% Scale the points so that the average distance from the origin is sqrt(2).
+avgDist = mean(sqrt(transx.^2 + transy.^2));
+scale = sqrt(2)/avgDist;
+
+sim = [scale 0 -scale*mux;
+       0 scale -scale*muy;
+       0 0 1];
+
+normP1 = sim*p2;
 
 % Compute H.
-H2to1 = computeH(normBS(p1), normBS(p2));
+H2to1 = computeH(normP1, normP2);
 
 
 end

BenFiles/normBS.m

@@ -2,19 +2,26 @@
 % Section 5.1 Sensitivity to Normalization
 
 function [ normP ] = normBS( p )
-% Input: p is 3xN set of points in 2d, where N is the number of points
-% Output: normP is a 3xN set of points in 2d, where N is the number of
-% points, but normalized.
+    % Input: p is 3xN set of points in 2d, where N is the number of points
+    % Output: normP is a 3xN set of points in 2d, where N is the number of
+    % points, but normalized.
 
-% Translate the centroid of the points to the origin.
-mu = mean(p(:));
-trans = p - mu;
+%     % Translate the centroid of the points to the origin.
+%     mux = mean(p(1,:));
+%     muy = mean(p(2,:));
+%     transx = p(1,:) - mux;
+%     transy = p(2,:) - muy;
+% 
+%     % Scale the points so that the average distance from the origin is sqrt(2).
+%     avgDist = mean(sqrt(transx.^2 + transy.^2));
+%     scale = sqrt(2)/avgDist;
+%     
+%     sim = [scale 0 -scale*mux;
+%            0 scale -scale*muy;
+%            0 0 1];
+%        
+%     normP = sim*p;
+
 
 
-% Scale the points so that the average distance from the origin is sqrt(2).
-avgDist = sum(sqrt(trans(1,:).^2 + trans(2,:).^2))/length(trans(1,:));
-
-normP = trans * sqrt(2) / avgDist;
-
-end
-
+end

BenFiles/normCompare.m

@@ -6,7 +6,7 @@
 close all
 clear all
 
-p = 100 * [-2 -1 0 1 2; 10 2 1 2 10];
+p = 100 * [-2 -1 0 1 2; 10 2 1 2 10; 1 1 1 1 1];
 ptest = 100 * [0; 3; 1];
 
 
@@ -28,12 +28,12 @@
     % Generate the homographies between the original and corrupted data.
     % The homographies produce 3x3 transformation matrices. 
     H = computeH(p, p_corrupt);
-    Hnorm = computeH_norm(p, p_corrupt);
+    [sim, Hnorm] = computeH_norm(p, p_corrupt);
 
 
     noNormed(:,i) = H*ptest;
     %noNormed(:,i) = noNormed(:,i)./noNormed(3,i); % normalize
-    normed(:,i) = Hnorm*ptest;
+    normed(:,i) = inv(sim)*Hnorm*ptest;
     %normed(:,i) = normed(:,i)./normed(3,i); % normalize
 
 
@@ -43,8 +43,8 @@
 % Plot the resulting point sets in a single plot in order to compare the
 % normalized and un-normalized results. 
 
-noNormed = normBS(noNormed);
-normed = normBS(normed);
+% noNormed = normBS(noNormed);
+% normed = normBS(normed);
 
 figure;
 hold on;