如何在Matlab中利用MSER和HOG进行匹配

Question

我想知道在Matlab中是否有MSER和HOG的图像匹配的完整实现。目前我正在使用VLFeat，但在执行图像匹配时发现了一些困难。有什么帮助吗？

顺便说一句，我已经在VLFeat -Matlab环境中尝试了下面的代码，但不幸的是无法执行匹配。

%Matlab code
%
pfx = fullfile(vl_root,'figures','demo') ;
randn('state',0) ;
rand('state',0) ;
figure(1) ; clf ;

Ia = imread(fullfile(vl_root,'data','roofs1.jpg')) ;
Ib = imread(fullfile(vl_root,'data','roofs2.jpg')) ;

Ia = uint8(rgb2gray(Ia)) ;
Ib = uint8(rgb2gray(Ib)) ;

[ra,fa] = vl_mser(I,'MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ;
[rb,fb] = vl_mser(I,'MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ;

[matches, scores] = vl_ubcmatch(fa, fb);

figure(1) ; clf ;
imagesc(cat(2, Ia, Ib));
axis image off ;
vl_demo_print('mser_match_1', 1);

figure(2) ; clf ;
imagesc(cat(2, Ia, Ib));

xa = ra(1, matches(1,:));
xb = rb(1, matches(2,:)) + size(Ia,2);
ya = ra(2, matches(1,:));
yb = rb(2,matches(2,:));

hold on ;
h = line([xa ; xb], [ya ; yb]);
set(h, 'linewidth', 1, 'color', 'b');

vl_plotframe(ra(:,matches(1,:)));
rb(1,:) = fb(1,:) + size(Ia,2);
vl_plotframe(rb(:,mathces(2,:)));
axis image off ;

vl_demo_print('mser_match_2', 1);

%%%%%%

Answer 1

有几个问题。首先，代码有几个错误，不能按原样运行。我已经在下面粘贴了我的工作版本。

更重要的是，您正在尝试使用SIFT特征匹配函数来匹配MSER椭球。这根本不起作用，因为SIFT基于局部图像梯度给出了一个非常高维的特征向量，而MSER检测器只是给出了一个边界椭球。

VLFeat似乎没有包含MSER匹配函数，因此您可能必须自己编写。看看最初的MSER论文，了解他们是如何进行匹配的：

"Robust wide-baseline stereo from maximally stable extremal regions", Matas et al. 2002

% Read the input images
Ia = imread(fullfile(vl_root,'data','roofs1.jpg')) ;
Ib = imread(fullfile(vl_root,'data','roofs2.jpg')) ;

% Convert to grayscale
Ia = uint8(rgb2gray(Ia)) ;
Ib = uint8(rgb2gray(Ib)) ;

% Find MSERs
[ra,fa] = vl_mser(Ia, 'MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ;
[rb,fb] = vl_mser(Ib, 'MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ;

% Match MSERs
[matches, scores] = vl_ubcmatch(fa, fb);

% Display the original input images
figure(1); clf;
imagesc(cat(2, Ia, Ib));
axis image off;
colormap gray;

% Display a second copy with the matches overlaid
figure(2) ; clf ;
imagesc(cat(2, Ia, Ib));
axis image off;
colormap gray;

xa = fa(1, matches(1,:));
ya = fa(2, matches(1,:));
xb = fb(1, matches(2,:)) + size(Ia,2);
yb = fb(2, matches(2,:));

hold on ;
h = line([xa ; xb], [ya ; yb]);
set(h, 'linewidth', 1, 'color', 'y');

Answer 2

我不知道是如何实现的，但MSER匹配在Matlab中是有效的。

下面的代码

file1 = 'roofs1.jpg';
file2 = 'roofs2.jpg';

I1 = imread(file1);
I2 = imread(file2);

I1 = rgb2gray(I1);
I2 = rgb2gray(I2);

% %Find the SURF features.
% points1 = detectSURFFeatures(I1);
% points2 = detectSURFFeatures(I2); 

points1 = detectMSERFeatures(I1);
points2 = detectMSERFeatures(I2); 

%Extract the features.
[f1, vpts1] = extractFeatures(I1, points1);
[f2, vpts2] = extractFeatures(I2, points2);

%Retrieve the locations of matched points. The SURF featurevectors are already normalized.
indexPairs = matchFeatures(f1, f2, 'Prenormalized', true) ;
matched_pts1 = vpts1(indexPairs(:, 1));
matched_pts2 = vpts2(indexPairs(:, 2));


figure; showMatchedFeatures(I1,I2,matched_pts1,matched_pts2,'montage');
legend('matched points 1','matched points 2');

给出了下面的图片

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