Curvilinear structure detections

基于曲率的图像特征检测技术

最新推荐文章于 2022-03-25 20:53:24 发布

转载最新推荐文章于 2022-03-25 20:53:24 发布 · 67 阅读

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#sketch

本文介绍了一种基于曲率的图像检测方法（PCBR），它利用曲线结构而非边缘进行图像特征提取，生成比梯度幅度图像更清晰的结构草图。通过修改Steger算法来获取曲线图像，并采用主曲率作为检测器名称。计算Hessian矩阵来获取最大和最小特征值图像，形成黑白对比的线条图像。

此部分参考处

As a structure-based detector, PCBR does not use edges, instead, it uses curvilinear structures, also called ridges. Curvilinear structures detection generates a single response for both lines and edges, producing a clearer structural sketch of an image than is usually provided by the gradient magnitude image. The Steger's algorithm ^[2] is modified to get the curvilinear images. As only the first step of this algorithm is used which is to calculate the principal curvature images, the principal curvature is adopted as the name of this detector. To get the principal curvature, the Hessian matrix is calculated:

$H(\mathbf{x}) = \begin{bmatrix} L_{xx}(\mathbf{x}) & L_{xy}(\mathbf{x})\\ L_{xy}(\mathbf{x}) & L_{yy}(\mathbf{x})\\ \end{bmatrix}$

where $L_{aa}(\mathbf{x})$ is second partial derivative of the image evaluated at point x in the $a$ direction and $L_{ab}(\mathbf{x})$ is the mixed partial second derivative of the image evaluated at point x in the $a$ and $b$ directions. The maximum and minimum eigenvalues of this matrix form two images which correspond to white lines on black background and black lines on white background.