function [ H,ok ] = estimateHomography( X1,X2 ) numMatches=size(X1,2) clear H score ok ; for t = 1:100 ； subset = vl_colsubset(1:numMatches, 4) ; A = [] ; for i = subset A = cat(1, A, kron(X1(:,i)', vl_hat(X2(:,i)))) ; end [U,S,V] = svd(A) ; H{t} = reshape(V(:,9),3,3) ; X2_ = H{t} * X1 ; du = X2_(1,:)./X2_(3,:) - X2(1,:)./X2(3,:) ; dv = X2_(2,:)./X2_(3,:) - X2(2,:)./X2(3,:) ; ok{t} = (du.*du + dv.*dv) < 6*6 ; score(t) = sum(ok{t}) ; end [score, best] = max(score) ; H = H{best} ; ok = ok{best} ; end

这是一个估计单应性矩阵的函数，输入参数 X1 和 X2 分别表示两个图像中匹配点的坐标，输出参数 H 表示估计得到的单应性矩阵，ok 表示哪些匹配点被用于估计单应性矩阵。该函数使用随机采样一致性算法进行估计。

相关问题

function [x1, x2]=FindMatch (I1,I2);

这段代码是一个 MATLAB 函数定义，函数名为 FindMatch，输入参数为 I1 和 I2，输出参数为 x1 和 x2。该函数的功能可能是在输入的两张图片 I1 和 I2 中找到匹配的特征点，并返回这些特征点的坐标。

但是，从这一行代码无法确定具体实现细节，还需要查看函数的实现代码才能了解其具体实现。

function [y1,y2] = matrix_mut(x1,x2,n)

As an AI language model, I do not have access to the context of this function. However, I can provide a general syntax for matrix multiplication using the function:

function [y1,y2] = matrix_mut(x1,x2,n)
y1 = x1 * x2;
y2 = x2 * x1;
end

Here, the function takes two matrices x1 and x2 as inputs and calculates their product, which is stored in y1. It also calculates the product of x2 and x1, which is stored in y2. The variable n is not used in this function.