马氏距离和NNDR的MATLAB代码

以下是马氏距离和NNDR的MATLAB代码示例：

马氏距离：

% 计算马氏距离
function distance = mahalanobisDistance(x, y, S)
% x和y是两个向量，S是协方差矩阵
diff = x - y;
distance = sqrt(diff' * inv(S) * diff);
end

使用马氏距离进行图像匹配的示例：

% 图像匹配
function [matches, scores] = imageMatching(img1, img2)
% img1和img2是要匹配的两张图像
% 使用SIFT算法提取特征点和特征描述子
[f1, d1] = vl_sift(single(rgb2gray(img1)));
[f2, d2] = vl_sift(single(rgb2gray(img2)));
% 使用FLANN库进行最近邻搜索
[index, distance] = vl_kdtreebuild(d2);
[matches, scores] = vl_ubcmatch(d1, d2, threshold);
% 使用马氏距离进行匹配筛选
for i = 1 : size(matches, 2)
x = f1(1:2, matches(1, i));
y = f2(1:2, matches(2, i));
S = cov(d2(:, index(1, matches(2, i))));
if mahalanobisDistance(x, y, S) > threshold
matches(:, i) = 0;
end
end
end

NNDR：

% 计算NNDR
function nndr = computeNNDR(dist1, dist2, threshold)
% dist1和dist2是两组距离，threshold是阈值
nndr = min(dist1 ./ dist2, dist2 ./ dist1) < threshold;
end

使用NNDR进行图像匹配的示例：

% 图像匹配
function [matches, scores] = imageMatching(img1, img2)
% img1和img2是要匹配的两张图像
% 使用SIFT算法提取特征点和特征描述子
[f1, d1] = vl_sift(single(rgb2gray(img1)));
[f2, d2] = vl_sift(single(rgb2gray(img2)));
% 使用FLANN库进行最近邻搜索
[index, distance] = vl_kdtreebuild(d2);
[matches, scores] = vl_ubcmatch(d1, d2, threshold);
% 使用NNDR进行匹配筛选
for i = 1 : size(matches, 2)
% 计算两组距离
dist1 = distance(matches(2, i), 1);
dist2 = distance(matches(2, i), 2);
if ~computeNNDR(dist1, dist2, threshold)
matches(:, i) = 0;
end
end
end