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2.部分仿真图预览
3.算法概述
SIFT 是一种从图像中提取独特不变特征的方法，其特点为基于图像的一些局部特征，而与图像整体的大小和旋转无关。并且该方法对于光照、噪声、仿射变换具有一定鲁棒性，同时能生成大量的特征点。SIFT (Scale-invariant feature transform)， 尺度不变特征转换，是一种图像局部特征提取算法，它通过在不同的尺度空间中寻找极值点（特征点，关键点）的精确定位和主方向，构建关键点描述符来提取特征。
4.部分源码
function [matchLoc1 matchLoc2] = siftMatch(img1, img2)
[des1, loc1] = sift(img1);
[des2, loc2] = sift(img2);
distRatio = 0.6;
% For each descriptor in the first image, select its match to second image.
des2t = des2'; % Precompute matrix transpose
matchTable = zeros(1,size(des1,1));
for i = 1 : size(des1,1)
dotprods = des1(i,:) * des2t; % Computes vector of dot products
[vals,indx] = sort(acos(dotprods)); % Take inverse cosine and sort results
% Check if nearest neighbor has angle less than distRatio times 2nd.
if (vals(1) < distRatio * vals(2))
matchTable(i) = indx(1);
else
matchTable(i) = 0;
end
end
img3 = appendimages(img1,img2);
% Show a figure with lines joining the accepted matches.
figure('Position', [100 100 size(img3,2) size(img3,1)]);
colormap('gray');
imagesc(img3);
hold on;
cols1 = size(img1,2);
for i = 1: size(des1,1)
if (matchTable(i) > 0)
line([loc1(i,2) loc2(matchTable(i),2)+cols1], ...
[loc1(i,1) loc2(matchTable(i),1)], 'Color', 'c');
end
end
hold off;
num = sum(matchTable > 0);
fprintf('Found %d matches.\n', num);
idx1 = find(matchTable);
idx2 = matchTable(idx1);
x1 = loc1(idx1,2);
x2 = loc2(idx2,2);
y1 = loc1(idx1,1);
y2 = loc2(idx2,1);
matchLoc1 = [x1,y1];
matchLoc2 = [x2,y2];
end
A170
