MATLAB特征点的检测与提取（1）

在MATLAB的Computer Vision System Toolbox™工具箱中，有FAST，Harris和Shi & Tomasi 角点检测子和SURF、MSER斑点检测子。这个工具箱还有SURF,FREAK,BRISK,LBP以及HOG描述子。

局部点的检测和提取：

一般用于：1）用于定位图像拼接或三维重建的锚点。2）在不需要图像分割的情况下，紧凑地表示图像内容以进行检测或分类。

1，图像配准与拼接MATLAB案例

第一步：加载需要拼接的图像；

% Load images.
buildingDir = fullfile(toolboxdir('vision'), 'visiondata', 'building');
buildingScene = imageSet(buildingDir);

% Display images to be stitched
montage(buildingScene.ImageLocation)

第二步：存储图像对；在I(n)和I(n-1)之间检测和匹配特征点；估计几何变换T(n)，从I(n)映射到I(n-1)；计算和转换映射I(n)成全景图像T(1)*...T(n-1)*T(n).

% Read the first image from the image set.
I = read(buildingScene, 1);

% Initialize features for I(1)
grayImage = rgb2gray(I);
points = detectSURFFeatures(grayImage);
[features, points] = extractFeatures(grayImage, points);

% Initialize all the transforms to the identity matrix. Note that the
% projective transform is used here because the building images are fairly
% close to the camera. Had the scene been captured from a further distance,
% an affine transform would suffice.
tforms(buildingScene.Count) = projective2d(eye(3));

% Iterate over remaining image pairs
for n = 2:buildingScene.Count

    % Store points and features for I(n-1).
    pointsPrevious = points;
    featuresPrevious = features;

    % Read I(n).
    I = read(buildingScene, n);

    % Detect and extract SURF features for I(n).
    grayImage = rgb2gray(I);
    points = detectSURFFeatures(grayImage);
    [features, points] = extractFeatures(grayImage, points);

    % Find correspondences between I(n) and I(n-1).
    indexPairs = matchFeatures(features, featuresPrevious, 'Unique', true);

    matchedPoints = points(indexPairs(:,1), :);
    matchedPointsPrev = pointsPrevious(indexPairs(:,2), :);

    % Estimate the transformation between I(n) and I(n-1).
    tforms(n) = estimateGeometricTransform(matchedPoints, matchedPointsPrev,...
        'projective', 'Confidence', 99.9, 'MaxNumTrials', 2000);

    % Compute T(1) * ... * T(n-1) * T(n)
    tforms(n).T = tforms(n-1).T * tforms(n).T;
end

开始使用projective2d outputLimits 方法来找到每个变换的输出限制。然后，使用输出限制来自动找到大致位于场景中心的图像。

imageSize = size(I);  % all the images are the same size
% Compute the output limits  for each transform
for i = 1:numel(tforms)
    [xlim(i,:), ylim(i,:)] = outputLimits(tforms(i), [1 imageSize(2)], [1 imageSize(1)]);
end

接下来，计算每个变换的平均极限X，并找到位于中心的图像。这里只使用限制X，因为场景是水平的。如果使用另一组图像，则可能需要使用X和Y限制来找到中心图像。

avgXLim = mean(xlim, 2);

[~, idx] = sort(avgXLim);

centerIdx = floor((numel(tforms)+1)/2);

centerImageIdx = idx(centerIdx);
Tinv = invert(tforms(centerImageIdx));

for i = 1:numel(tforms)
    tforms(i).T = Tinv.T * tforms(i).T;
end

第三步：初始化全景图像

for i = 1:numel(tforms)
    [xlim(i,:), ylim(i,:)] = outputLimits(tforms(i), [1 imageSize(2)], [1 imageSize(1)]);
end

% Find the minimum and maximum output limits
xMin = min([1; xlim(:)]);
xMax = max([imageSize(2); xlim(:)]);

yMin = min([1; ylim(:)]);
yMax = max([imageSize(1); ylim(:)]);

% Width and height of panorama.
width  = round(xMax - xMin);
height = round(yMax - yMin);

% Initialize the "empty" panorama.
panorama = zeros([height width 3], 'like', I);

第四步：创造全景图像

blender = vision.AlphaBlender('Operation', 'Binary mask', ...
    'MaskSource', 'Input port');

% Create a 2-D spatial reference object defining the size of the panorama.
xLimits = [xMin xMax];
yLimits = [yMin yMax];
panoramaView = imref2d([height width], xLimits, yLimits);

% Create the panorama.
for i = 1:buildingScene.Count

    I = read(buildingScene, i);

    % Transform I into the panorama.
    warpedImage = imwarp(I, tforms(i), 'OutputView', panoramaView);

    % Overlay the warpedImage onto the panorama.
    panorama = step(blender, panorama, warpedImage, warpedImage(:,:,1));
end

figure
imshow(panorama)

MATLAB特征点的检测与提取（1）

猜你喜欢