採用結構光進行掃描檢測時，需要提取鐳射條紋的中心線，本文采用經典的Steger演算法提取光條中心。

Steger演算法原理

Steger演算法基於Hessian矩陣，能夠實現光條中心亞畫素精度定位：首先通過Hessian矩陣能夠得到光條的法線方向，然後在法線方向利用泰勒展開得到亞畫素位置。
對於影象中鐳射條紋上的任意一點(x,y)，Hessian矩陣可以表示為：

示例程式碼

根據Steger演算法的原理，藉助opencv的Mat資料結構，實現Steger演算法如下：

void StegerLine()
{ 
    Mat img0 = imread("image_0.png", 1);
    Mat img;
    cvtColor(img0, img0, CV_BGR2GRAY);
    img = img0.clone();

    //高斯濾波
    img.convertTo(img, CV_32FC1);
    GaussianBlur(img, img, Size(0, 0), 6, 6);

    //一階偏導數
    Mat m1, m2;
    m1 = (Mat_<float>(1, 2) << 1, -1);  //x偏導
 

    m2 = (Mat_<float>(2, 1) << 1, -1);  //y偏導

    Mat dx, dy;
    filter2D(img, dx, CV_32FC1, m1);
    filter2D(img, dy, CV_32FC1, m2);

    //二階偏導數
    Mat m3, m4, m5;
    m3 = (Mat_<float>(1, 3) << 1, -2, 1);   //二階x偏導
    m4 = (Mat_<float>(3, 1) << 1, -2, 1);   //二階y偏導
    m5 = (Mat_<float>(2, 2) << 1, -1, -1, 1);   //二階xy偏導

    Mat dxx, dyy, dxy;
    filter2D(img, dxx, CV_32FC1, m3);
    filter2D(img, dyy, CV_32FC1, m4);
    filter2D(img, dxy, CV_32FC1, m5);

    //hessian矩陣
    double maxD = -1;
    int imgcol = img.cols;
    int imgrow = img.rows;
    vector<double> Pt;
    for (int i=0;i<imgcol;i++)
    {
        for (int j=0;j<imgrow;j++)
        {
            if (img0.at<uchar>(j,i)>200)
            {
                Mat hessian(2, 2, CV_32FC1);
                hessian.at<float>(0, 0) = dxx.at<float>(j, i);
                hessian.at<float>(0, 1) = dxy.at<float>(j, i);
                hessian.at<float>(1, 0) = dxy.at<float>(j, i);
                hessian.at<float>(1, 1) = dyy.at<float>(j, i);

                Mat eValue;
                Mat eVectors;
                eigen(hessian, eValue, eVectors);

                double nx, ny;
                double fmaxD = 0;
                if (fabs(eValue.at<float>(0,0))>= fabs(eValue.at<float>(1,0)))  //求特徵值最大時對應的特徵向量
                {
                    nx = eVectors.at<float>(0, 0);
                    ny = eVectors.at<float>(0, 1);
                    fmaxD = eValue.at<float>(0, 0);
                }
                else
                {
                    nx = eVectors.at<float>(1, 0);
                    ny = eVectors.at<float>(1, 1);
                    fmaxD = eValue.at<float>(1, 0);
                }

                double t = -(nx*dx.at<float>(j, i) + ny*dy.at<float>(j, i)) / (nx*nx*dxx.at<float>(j,i)+2*nx*ny*dxy.at<float>(j,i)+ny*ny*dyy.at<float>(j,i));

                if (fabs(t*nx)<=0.5 && fabs(t*ny)<=0.5)
                {
                    Pt.push_back(i);
                    Pt.push_back(j);
                }
            }
        }
    }

    for (int k = 0;k<Pt.size()/2;k++)
    {
        Point rpt;
        rpt.x = Pt[2 * k + 0];
        rpt.y = Pt[2 * k + 1];
        circle(img0, rpt, 1, Scalar(0, 0, 255));
    }

    imshow("result", img0);
    waitKey(0);
}

示例結果

1. Steger C. An unbiased detector of curvilinear structures. IEEE Trans Pattern Anal Mach Intell[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998, 20(2):113-125.