Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D

作     者：Pieta, Pawel Tomasz Dahl, Anders Bjorholm Frisvad, Jeppe Revall Bigdeli, Siavash Arjomand Christensen, Anders Nymark 

作者机构：Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：9766-9779页

核心收录：

基　　金：Innovation Fund Denmark [0223-00041B] 

主　　题：Tensors Filtering theory Three-dimensional displays Information filters Image edge detection Feature extraction Standards Smoothing methods Shape Hands 3D image processing scale-space structural analysis structure tensor 

摘      要：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.