A 5
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Magdalena Mazur 89739015 April 8th, 2008
CS425 Assignment 5 – Object and location recognition
This booklet contains only the first part of the assignment. % % % % % % % % % %
num = match(image1, image2) This function reads two images and their associated keypoints, and then displays lines connecting the keypoints from each image (these are correct matches) dependeing on the threshold specified. The arguments image1 and image2 are the file names without the file extensions. It returns the number of matches displayed. Example: match('scene','book');
function num = match(image1, image2, threshold) % Load the images and keypoint descriptors and locations % readkeys returns the arguments [image, descriptors, locVectors]: % [im1, des1, loc1] = readkeys(image1); [im2, des2, loc2] = readkeys(image2); % Create a new image showing the two images side by side. im3 = appendimages(im1,im2); % Show a figure with the joined images and overlaid lines that are % drawns between the first 5 keypoints in each image (these are just % random matches) imshow(im3); hold on; num=0; %set the loop boundaries v = size(des1); end1=v(1); cols1 = size(im1,2); rows1=size(im1,1); v1 = size(des2); e = v1(1); %for all descriptors in im1 loop here for i=1:end1 %over all descriptors in im2 loop here, get angle between these descriptors and descriptors in im1 for j=1:e dotpr = dot(des1(i,:), des2(j,:)); angles(j) = acos(dotpr); end %get the two smallest angles a = angles'; [Y, I] = sort(a); smallest = I(1) second = I(2) an = angles(smallest) an2=angles(second) %if ratio of the two smallest angles below the threshold mark the %descriptor if(angles(smallest)/angles(second)
end
end
The outcomes of various thresholds are presented below:
t=0.75
t=0.5
t=0.85
t=0.6
The best matching was achieved with a threshold choice of 0.6. Threshold above .75 found many false matches. Therefore for further optimization higher values of the threshold should be avoided. Especially, since some of the methods (such as RANSAC) are not feasible for hight percentage of outliers (above 50%). (if using RANSAC, the matches would be used as 'the model' and false matches would be later on eliminated in ransac iterations)
CS425 Assignment 5 – Object and location recognition
This booklet contains only the first part of the assignment. % % % % % % % % % %
num = match(image1, image2) This function reads two images and their associated keypoints, and then displays lines connecting the keypoints from each image (these are correct matches) dependeing on the threshold specified. The arguments image1 and image2 are the file names without the file extensions. It returns the number of matches displayed. Example: match('scene','book');
function num = match(image1, image2, threshold) % Load the images and keypoint descriptors and locations % readkeys returns the arguments [image, descriptors, locVectors]: % [im1, des1, loc1] = readkeys(image1); [im2, des2, loc2] = readkeys(image2); % Create a new image showing the two images side by side. im3 = appendimages(im1,im2); % Show a figure with the joined images and overlaid lines that are % drawns between the first 5 keypoints in each image (these are just % random matches) imshow(im3); hold on; num=0; %set the loop boundaries v = size(des1); end1=v(1); cols1 = size(im1,2); rows1=size(im1,1); v1 = size(des2); e = v1(1); %for all descriptors in im1 loop here for i=1:end1 %over all descriptors in im2 loop here, get angle between these descriptors and descriptors in im1 for j=1:e dotpr = dot(des1(i,:), des2(j,:)); angles(j) = acos(dotpr); end %get the two smallest angles a = angles'; [Y, I] = sort(a); smallest = I(1) second = I(2) an = angles(smallest) an2=angles(second) %if ratio of the two smallest angles below the threshold mark the %descriptor if(angles(smallest)/angles(second)
end
end
The outcomes of various thresholds are presented below:
t=0.75
t=0.5
t=0.85
t=0.6
The best matching was achieved with a threshold choice of 0.6. Threshold above .75 found many false matches. Therefore for further optimization higher values of the threshold should be avoided. Especially, since some of the methods (such as RANSAC) are not feasible for hight percentage of outliers (above 50%). (if using RANSAC, the matches would be used as 'the model' and false matches would be later on eliminated in ransac iterations)
