使用matlab写一个sift算法
时间: 2023-10-02 07:08:18 浏览: 132
SIFT (Scale-Invariant Feature Transform) 算法是一种用于图像特征提取的算法,它可以在不同尺度和旋转角度下提取出稳定的关键点,并且对于光照、噪声等因素也具有一定的鲁棒性。下面是使用 MATLAB 实现 SIFT 算法的基本步骤:
1. 读入图像并转换为灰度图像。
```matlab
img = imread('image.jpg');
gray_img = rgb2gray(img);
```
2. 构建高斯金字塔。
```matlab
octaves = 4; % 金字塔层数
scales = 5; % 每层尺度数
k = 2^(1/scales); % 尺度之间的比例因子
% 构建高斯核
sigma = 1.6;
hsize = 2*ceil(3*sigma)+1;
G = fspecial('gaussian', hsize, sigma);
% 初始化金字塔
G_pyr = cell(octaves, scales+3);
G_pyr{1,1} = gray_img;
% 构建高斯金字塔
for i = 1:octaves
for j = 2:scales+3
G_pyr{i,j} = imresize(conv2(G_pyr{i,j-1}, G, 'same'), 0.5);
end
G_pyr{i+1,1} = imresize(G_pyr{i,scales+3}, 2);
end
```
3. 构建差分金字塔。
```matlab
D_pyr = cell(octaves, scales+2);
for i = 1:octaves
for j = 1:scales+2
D_pyr{i,j} = G_pyr{i,j+1} - G_pyr{i,j};
end
end
```
4. 在差分金字塔中寻找极值点。
```matlab
thresh = 0.03; % 阈值
r = 10; % 极值点邻域半径
c = 0.03; % 对比度阈值
extrema = [];
for i = 1:octaves
for j = 2:scales+1
[h, w] = size(D_pyr{i,j});
for x = r+1:h-r-1
for y = r+1:w-r-1
patch = D_pyr{i,j}(x-r:x+r, y-r:y+r);
val = patch(r+1,r+1);
if abs(val) < thresh
continue;
end
ismax = true;
ismin = true;
for u = -1:1
for v = -1:1
if u == 0 && v == 0
continue;
end
if val < 0
if patch(r+1,r+1) > patch(r+1+u,r+1+v)
ismax = false;
elseif patch(r+1,r+1) < patch(r+1+u,r+1+v)
ismin = false;
end
else
if patch(r+1,r+1) < patch(r+1+u,r+1+v)
ismax = false;
elseif patch(r+1,r+1) > patch(r+1+u,r+1+v)
ismin = false;
end
end
end
end
if ismax || ismin
contrast = abs(val);
for s = j-1:j+1
for p = x-1:x+1
for q = y-1:y+1
if s == j && p == x && q == y
continue;
end
if G_pyr{i,s}(p,q) < c*contrast
ismax = false;
ismin = false;
break;
end
end
if ~ismax || ~ismin
break;
end
end
if ~ismax || ~ismin
break;
end
end
if ismax || ismin
extrema = [extrema; i, j, x, y];
end
end
end
end
end
end
```
5. 计算关键点的尺度和方向。
```matlab
bins = 36; % 方向直方图的桶数
sigma = 1.5; % 关键点邻域高斯权重的标准差
keypoints = [];
for i = 1:size(extrema,1)
octave = extrema(i,1);
scale = extrema(i,2);
x = extrema(i,3);
y = extrema(i,4);
patch = G_pyr{octave,scale}(x-8:x+7, y-8:y+7);
dx = (patch(:,[2:end,end]) - patch(:,[1,1:end-1])) / 2;
dy = (patch([2:end,end],:) - patch([1,1:end-1],:)) / 2;
mag = sqrt(dx.^2 + dy.^2);
ori = atan2(dy, dx) * 180 / pi;
ori = mod(ori, 360);
weight = fspecial('gaussian', [16, 16], sigma);
hist = zeros(bins, 1);
for j = 1:16
for k = 1:16
bin = floor(ori(j,k) / 360 * bins) + 1;
hist(bin) = hist(bin) + mag(j,k) * weight(j,k);
end
end
[~, max_idx] = max(hist);
ori = (max_idx-1) * 360 / bins;
keypoints = [keypoints; x, y, scale, ori];
end
```
这样就完成了 SIFT 算法的实现。注意:这只是一个简单的实现,实际上 SIFT 算法还有很多优化和细节处理,例如非极大值抑制、关键点插值等,可以参考原始论文和相关资料进行进一步学习和实现。
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