通过特征中心对齐滤波器宽度，提升2D/3D结构分析的准确性与自动化程度。
Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D
- 以特征尺寸直接设定导数滤波器宽度，减少参数依赖。
- 引入环形滤波器，将响应从边缘精准移至中心，提升定位精度。
- 可直接用于提取多种结构参数，适合无经验用户快速部署。
结构张量方法常用于2D和3D图像结构分析，但其结果对参数选择敏感。本文通过在一阶结构张量尺度空间中，直接将导数滤波器宽度与图像特征尺寸关联，简化了参数选择。引入环形滤波器步骤，用更精确的方式将导数滤波器响应从特征边缘转移至中心，替代传统的高斯积分/平滑。进一步展示了如何利用提取的结构度量校正尺度图中的已知偏差，实现2D和3D下特征尺寸的可靠表征。相比传统一阶结构张量或先前尺度空间方法，本方法显著提升准确性，可作为无需用户干预的通用工具，用于提取多种结构参数。
原文摘要 · Abstract (English)
The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user's choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.
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