特征检测与提取
一、局部特征提取
- 检测SURF特征点
points = detectSURFFeatures(I);- 提取特征描述子
[features,validPoints] = extractFeatures(I,points);二、特征匹配
- 特征描述子匹配
indexPairs = matchFeatures(features1,features2);- 显示匹配的特征点
showMatchedFeatures(I1,I2,matchedPoints1,matchedPoints2);- 从匹配点对估计几何变换
tform = estimateGeometricTransform(matchedPoints1,matchedPoints2,transformType);该函数使用随机样本一致性（RANSAC）算法的变体MSAC算法实现，去除误匹配点
三、图像拼接
图像拼接大致上由两部分组成：图像配准与图像融合。图像配准即采用算子寻找、匹配特征，进而找出重叠位置，确定变换关系等。图像融合则完成最终图像的生成与边界处理等。
或者可以分为四个步骤：特征点提取、特征点的匹配、变换矩阵的计算和图像融合；
图像配准即上面两节内容，而将变换矩阵应用于图像的几何变换与图像融合则是上一张的内容。由此即可完成整个图像拼接过程。
%基于特征点的图像拼接
[file1,filepath1]=uigetfile('*');%获取文件名
[file2,filepath2]=uigetfile('*');
file1=fullfile(filepath1,file1);
file2=fullfile(filepath2,file2);
I1=imread(file1);%读取图片
I2=imread(file2);
imgs=[I1,I2];
figure,imshow(imgs);%并排显示两幅待拼接图像
title('待拼接图像');
pause;
img1=rgb2gray(I1);
img2=rgb2gray(I2);
imageSize=size(img1);
p1=detectSURFFeatures(img1);
p2=detectSURFFeatures(img2);%检测SURF特征点
imshow(I1); hold on;
plot(p1.selectStrongest(50));
title('图1最强50特征点');
hold off;
pause;
imshow(I2); hold on;
plot(p2.selectStrongest(50));%显示强度最强的50个特征点
title('图2最强50特征点');
hold off;
pause;
[img1Features, p1] = extractFeatures(img1, p1);%使用64维向量表示特征描述子,
%第一个返回的参数即为每个特征点对应的特征描述子，第二个参数是特征点
[img2Features, p2] = extractFeatures(img2, p2);
boxPairs = matchFeatures(img1Features, img2Features);%特征描述子匹配
matchedimg1Points = p1(boxPairs(:, 1));%第二个参数:可以不加，因为其为n行1列的结构体数组
matchedimg2Points = p2(boxPairs(:, 2));
showMatchedFeatures(I1, I2, matchedimg1Points, ...
matchedimg2Points, 'montage');
title('Putatively Matched Points (Including Outliers)');%显示匹配的特征描述子
pause;
[tform, inlierimg2Points, inlierimg1Points] = ...
estimateGeometricTransform(matchedimg2Points, matchedimg1Points, 'projective');%射影变换，tfrom映射点对1内点到点对2内点
%该函数使用随机样本一致性（RANSAC）算法的变体MSAC算法实现，去除误匹配点
%The returned geometric transformation matrix maps the inliers in matchedPoints1
%to the inliers in matchedPoints2.返回的几何映射矩阵映射第一参数内点到第二参数内点
showMatchedFeatures(I1, I2, inlierimg1Points, ...
inlierimg2Points, 'montage');
title('Matched Points (Inliers Only)');
pause;
%load('stiching.mat');
% % % % % %%%%%%%%%%%%%%%%%%%%%%%%显示差异%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Rfixed = imref2d(size(I1));
[registered2, Rregistered] = imwarp(I2, tform);
imshowpair(I1,Rfixed,registered2,Rregistered,'blend');
title('图像差异');
pause;
[xlim, ylim] = outputLimits(tform, [1 imageSize(2)], [1 imageSize(1)]);%输出坐标范围 x:2000~6000 y:41~3000
% 找到输出空间限制的最大最小值
xMin = min([1; xlim(:)]);%1
xMax = max([imageSize(2); xlim(:)]);%6276
yMin = min([1; ylim(:)]);
yMax = max([imageSize(1); ylim(:)]);%3272
% 全景图的宽高
width = round(xMax - xMin);
height = round(yMax - yMin);
%创建2D空间参考对象定义全景图尺寸
xLimits = [xMin xMax];
yLimits = [yMin yMax];
panoramaView = imref2d([height width], xLimits, yLimits);
% 变换图片到全景图.
unwarpedImage = imwarp(I1,projective2d(eye(3)), 'OutputView', panoramaView);
warpedImage = imwarp(I2, tform, 'OutputView', panoramaView);
% % %%%%%%%%%%%%%%%%%%%%%%%%%%渐入渐出融合%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MAKE MASKS FOR BOTH IMAGES
% warpedImage(isnan(warpedImage))=0;
maskA = (warpedImage(:,:,1)>0 |warpedImage(:,:,2)>0 | warpedImage(:,:,3)>0);%变换图像掩膜
newImage = zeros(size(warpedImage));
newImage(1:size(I1,1), 1: size(I1,2),:) = I1;
mask = (newImage(:,:,1)>0 | newImage(:,:,2)>0 | newImage(:,:,3)>0);%非变换图像掩膜
mask = and(maskA, mask);%重叠区掩膜
% GET THE OVERLAID REGION
[~,col] = find(mask);
left = min(col);
right = max(col);%获得重叠区左右范围
mask = ones(size(mask));
mask(:,left:right) = repmat(linspace(0,1,right-left+1),size(mask,1),1);%复制平铺矩阵
% BLEND EACH CHANNEL
warpedImage=double(warpedImage);
warpedImage(:,:,1) = warpedImage(:,:,1).*mask;
warpedImage(:,:,2) = warpedImage(:,:,2).*mask;
warpedImage(:,:,3) = warpedImage(:,:,3).*mask;
% REVERSE THE ALPHA VALUE
mask(:,left:right) = repmat(linspace(1,0,right-left+1),size(mask,1),1);
newImage(:,:,1) = newImage(:,:,1).*mask;
newImage(:,:,2) = newImage(:,:,2).*mask;
newImage(:,:,3) = newImage(:,:,3).*mask;
newImage(:,:,1) = warpedImage(:,:,1) + newImage(:,:,1);
newImage(:,:,2) = warpedImage(:,:,2) + newImage(:,:,2);
newImage(:,:,3) = warpedImage(:,:,3) + newImage(:,:,3);
newImage=uint8(newImage);
imshow(newImage);
title('渐入渐出融合');