Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D
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 th...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.2409.13389 |