首页 > 专利技术

图像拼接（八）：拼接多幅图像+Matlab实现+StanfordOpenCourse

图像拼接（⼋）：拼接多幅图像+Matlab实现+StanfordOpenCourse

本博客与以下⽂档资料⼀起服⽤效果更佳。

Matlab源码地址：

开始正⽂。

梳理⼀下本篇博客图像拼接的原理：

特征检测：SIFT⾓点检测

特征描述：SIFT描述⼦

特征匹配：暴⼒搜索+欧⽒距离

求变换矩阵：最⼩⼆乘法+RANSAC+仿射变换

拼接多幅图像

完整实现⼀个SIFT都很⿇烦，也并没必要。本博客是在斯坦福⼤学计算机视觉课程⼤作业的基础上实现的，读者也可以按课程PPT及作业pdf指导书，⾃⼰实现⼀遍，定会加深⾃⼰对其中关键算法的理解。

本博客根据其作业指导书，展⽰其中⼀些算法的Matlab代码实现。

<；译>

#1.引⽂

全景拼接是计算机视觉领域取得的⼀项早期成就。在2007年，Brown 和 Lowe发表了著名的图像拼接论⽂。⾃打那以后，⾃动全景拼接技术受到⼴泛应⽤，例如Google街景地图、智能⼿机上的全景照⽚、拼接软件⽐如Photosynth和AutoStitch。

在这个⼤作业中，我们会从多幅图像中匹配SIFT关键点，来构建单幅全景图⽚。这涉及以下任务：

使⽤⾼斯差分（DoG）检测器关键点，返回它的坐标位置和尺度。（这⼀步已经提供给你）

给⼀副图像的每个关键点构建SIFT描述⼦。

从两幅不同的图像中⽐较两组SIFT描述⼦，到匹配点。

给定⼀个关键点匹配的列表，使⽤最⼩⼆乘法到仿射变换矩阵，这个矩阵能将iamge1上的位置映射到image2上的位置。

使⽤RANSAC使仿射变换矩阵的估计具有更好的鲁棒性。

给定变换矩阵，使⽤它变换（平移、尺度、或者倾斜）image1，将它覆盖到image2上⾯，构建⼀个全景图。（这⼀步已经提供给你）

在真实世界场景中的特定例⼦中，把多幅图像拼接在⼀起。

<；译>

#2.构建SIFT描述⼦

复习SIFT算法的讲义PPT，编写给定的SIFTDescriptor.m，来为每个DoG关键点，产⽣SIFT关键点描述⼦。注意关键点的位置和尺度已经提供给你，使⽤的是⾼斯⾦字塔。

运⾏提供的EvaluateSIFTDescriptor.m，检查你的实现。

防摔玻璃杯这⼀步需要⾃⼰写的代码包括计算图像导数（x和y⽅向）、梯度（⼤⼩和⽅向）、计算邻域块的主⽅向、每个采样点的梯度相对⽅向、计算直⽅图的变量（⽅向箱⼦的划分，每个箱⼦的幅值）、计算梯度直⽅图以及将其串接进⼀个1*128的数组中。

代码提供了标准的结果数据和测试代码，经过测试，我编写的SIFT与标准数据偏差明显。

注：感谢yuanyuan12222的评论指正。出现偏差的原因是：主⽅向选取没有向下取（308⾏注释有说明），将312⾏代码direction

=2pi loc(1)/num_bins；改为direction =2pi(loc(1)-1)/num_bins；就OK了。

说明我的某部分实现不符合标准实现。但由最后的拼接效果来看，实现是可⽤的。

我的实现（Matlab代码）：

SIFTDescriptor.m

tomgrofunction descriptors = SIFTDescriptor(pyramid, keyPtLoc, keyPtScale)

% SIFTDescriptor Build SIFT descriptors from image at detected key points'

% location with detected key points' scale and angle

% INPUT:

% pyramid: Image pyramid. pyramid{i} is a rescaled version of the

% original image, in grayscale double format

% keyPtLoc: N * 2 matrix, each row is a key point pixel location in

% pyramid{round(keyPtScale)}. So pyramid{round(keyPtScale)}(y,x) is the center of the keypoint

% keyPtScale: N * 1 matrix, each entry holds the index in the Gaussian

% pyramid for the keypoint. Earlier code interpolates things, so this is a

% double, but we'll just round it to an integer.

红外焦平面探测器

% OUTPUT:

% descriptors: N * 128 matrix, each row is a feature descriptor

% Precompute the gradients at all pixels of all pyramid scales

% This is a cell array, which is like an ArrayList that holds matrices.

% You use {} to index into it, like this: magnitude = grad_mag{1}

grad_theta = cell(length(pyramid),1);

grad_mag = cell(length(pyramid),1);

for scale = 1:length(pyramid)

currentImage = pyramid{scale}; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%双层玻璃茶杯

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% YOUR CODE HERE %

% Read the doc for filter2. %

% Use with the filter [-1 0 1] to fill in img_dx, %

% and the filter [-1;0;1] to fill in img_dy. %

% Please use the filter2 'same' option so %

% the result will be the same size as the image. %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % gradient image, for gradients in x direction.

img_dx = zeros(size(currentImage));

img_dx=filter2([-1 0 1],currentImage);

% gradients in y direction.

img_dy = zeros(size(currentImage));

img_dy=filter2([-1;0;1],currentImage);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% END OF YOUR CODE %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% YOUR CODE HERE %

% Use img_dx and img_dy to compute the magnitude %

% and angle of the gradient at each pixel. %

% store them in grad_mag{scale} and grad_theta{scale} respectively. %

% The atan2 function will be helpful for calculating angle %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Calculate the magnitude and orientation of the gradient.

grad_mag{scale} = zeros(size(currentImage));

grad_theta{scale} = zeros(size(currentImage));

grad_mag{scale}=sqrt(img_dx.^2+img_dy.^2);

grad_theta{scale}=atan2(img_dy,img_dx);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% END OF YOUR CODE %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % atan2 gives angles from -pi to pi. To make the histogram code

% easier, we'll change that to 0 to 2*pi.

grad_theta{scale} = mod(grad_theta{scale}, 2*pi);

end

% The number of bins into which each gradient vector will be placed.

num_angles = 8;

% The patch extracted around each keypoint will be divided into a grid

% of num_histograms x num_histograms.

num_histograms = 4;

% Each histogram covers an area "pixelsPerHistogram" wide and tall

pixelsPerHistogram = 4;

% For each keypoint we will extract a region of size

% patch_size x patch_size centered at the keypoint.

patch_size = num_histograms * pixelsPerHistogram;

% Number of keypoints that were found by the DoG blob detector

N = size(keyPtLoc, 1);

% Initialize descriptors to zero

descriptors = zeros(N, num_histograms * num_histograms * num_angles);

% Iterate over all keypoints

for i = 1 : N

scale = round(keyPtScale(i));

% Find the window of pixels that contributes to the descriptor for the

% current keypoint.

xAtScale = keyPtLoc(i, 1);%center of the DoG keypoint in the pyramid{2} image

yAtScale = keyPtLoc(i, 2);

x_lo = round(xAtScale - patch_size / 2);

x_hi = x_lo+patch_size-1;

y_lo = round(yAtScale - patch_size / 2);

y_hi = y_lo+patch_size-1;

% These are the gradient magnitude and angle images from the

% correct scale level. You computed these above.

magnitudes = grad_mag{scale};

thetas = grad_theta{scale};

try

母线夹% Extract the patch from that window around the keypoint

patch_mag = zeros(patch_size,patch_size);

patch_theta = zeros(patch_size,patch_size);

patch_mag = magnitudes(y_lo:y_hi,x_lo:x_hi);

patch_theta = thetas(y_lo:y_hi,x_lo:x_hi);

catch err

% If any keypoint is too close to the boundary of the image

% then we just skip it.

continue;

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% YOUR CODE HERE: %

% %

% Express gradient directions relative to the dominant gradient direction %

% of this keypoint. %

% %

% HINT: Use the ComputeDominantDirection function below. %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Step 1: compute the dominant gradient direction of the patch

patch_angle_offset = ComputeDominantDirection(patch_mag, patch_theta);

% Step 2: change patch_theta so it's relative to the dominant direction

patch_theta = patch_theta-patch_angle_offset;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% END OF YOUR CODE %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% This line will re-map patch_theta into the range 0 to 2*pi

patch_theta = mod(patch_theta, 2*pi);

% Weight the gradient magnitudes using a gaussian function

patch_mag = patch_mag .* fspecial('gaussian', patch_size, patch_size / 2);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% YOUR CODE HERE: %

% %

% Compute the gradient histograms and concatenate them in the %

% feature variable to form a size 1x128 SIFT descriptor for this keypoint. %

% %

% HINT: Use the ComputeGradientHistogram function below. %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % The patch we have extracted should be subdivided into

% num_histograms x num_histograms cells, each of which is size

% pixelsPerHistogram x pixelsPerHistogram.

% Compute a gradient histogram for each cell, and concatenate

% the histograms into a single feature vector of length 128.

% Please traverse the patch row by row, starting in the top left,

% in order to match the given solution. E.g. if you use two

% nested 'for' loops, the loop over x should be the inner loop.

% (Note: Unlike the SIFT paper, we will not smooth a gradient across

% nearby histograms. For simplicity, we will just assign all

% gradient pixels within a pixelsPerHistogram x pixelsPerHistogram

% square to the same histogram.)

% Initializing the feature vector to size 0. Hint: you can

% concatenate the histogram descriptors to it like this:

% feature = [feature, histogram]

feature = [];

subdivided_patch_theta=zeros(pixelsPerHistogram,pixelsPerHistogram);

subdivided_patch_mag=zeros(pixelsPerHistogram,pixelsPerHistogram);

for y=1:4:13

for x=1:4:13

subdivided_patch_theta=patch_theta(y:y+3,x:x+3);

subdivided_patch_mag=patch_mag(y:y+3,x:x+3);

[histogram,angles]=ComputeGradientHistogram(num_angles,subdivided_patch_mag,subdivided_patch_theta);

feature=[feature,histogram];

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %

% END YOUR CODE %

% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Add the feature vector we just computed to our matrix of SIFT

% descriptors.

descriptors(i, :) = feature;

end

% Normalize the descriptors.

descriptors = NormalizeDescriptors(descriptors);

end

function [histogram, angles] = ComputeGradientHistogram(num_bins, gradient_magnitudes, gradient_angles)

% Compute a gradient histogram using gradient magnitudes and directions.

% Each point is assigned to one of num_bins depending on its gradient

% direction; the gradient magnitude of that point is added to its bin.

% INPUT

% num_bins: The number of bins to which points should be assigned.

% gradient_magnitudes, gradient angles:

% Two arrays of the same shape where gradient_magnitudes(i) and

% gradient_angles(i) give the magnitude and direction of the gradient

% for the ith point. gradient_angles ranges from 0 to 2*pi

% OUTPUT

% histogram: A 1 x num_bins array containing the gradient histogram. Entry 1 is

% the sum of entries in gradient_magnitudes whose corresponding

% gradient_angles lie between 0 and angle_step. Similarly, entry 2 is for

% angles between angle_step and 2*angle_step. Angle_step is calculated as

背光驱动

% 2*pi/num_bins.

% angles: A 1 x num_bins array which holds the histogram bin lower bounds.

% In other words, histogram(i) contains the sum of the

% gradient magnitudes of all points whose gradient directions fall

% in the range [angles(i), angles(i + 1))

angle_step = 2 * pi / num_bins;

本文发布于:2024-09-23 13:25:06，感谢您对本站的认可！

本文链接：https://www.17tex.com/tex/3/227419.html

上一篇：图像拼接方法总结

下一篇：发现作文800字（通用10篇）

标签：实现拼接关键点图像矩阵匹配

留言与评论（共有 0 条评论）