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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833649/ |
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| author | Pawel Tomasz Pieta Anders Bjorholm Dahl Jeppe Revall Frisvad Siavash Arjomand Bigdeli Anders Nymark Christensen |
| author_facet | Pawel Tomasz Pieta Anders Bjorholm Dahl Jeppe Revall Frisvad Siavash Arjomand Bigdeli Anders Nymark Christensen |
| author_sort | Pawel Tomasz Pieta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-99d963b4a32d4e18af899cd03a374325 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-99d963b4a32d4e18af899cd03a3743252025-08-20T03:00:02ZengIEEEIEEE Access2169-35362025-01-01139766977910.1109/ACCESS.2025.352722710833649Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3DPawel Tomasz Pieta0https://orcid.org/0009-0005-7634-6627Anders Bjorholm Dahl1https://orcid.org/0000-0002-0068-8170Jeppe Revall Frisvad2https://orcid.org/0000-0002-0603-3669Siavash Arjomand Bigdeli3https://orcid.org/0000-0003-2569-6473Anders Nymark Christensen4https://orcid.org/0000-0002-3668-3128Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkThe 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.https://ieeexplore.ieee.org/document/10833649/3D image processingscale-spacestructural analysisstructure tensor |
| spellingShingle | Pawel Tomasz Pieta Anders Bjorholm Dahl Jeppe Revall Frisvad Siavash Arjomand Bigdeli Anders Nymark Christensen Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D IEEE Access 3D image processing scale-space structural analysis structure tensor |
| title | Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D |
| title_full | Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D |
| title_fullStr | Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D |
| title_full_unstemmed | Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D |
| title_short | Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D |
| title_sort | feature centered first order structure tensor scale space in 2d and 3d |
| topic | 3D image processing scale-space structural analysis structure tensor |
| url | https://ieeexplore.ieee.org/document/10833649/ |
| work_keys_str_mv | AT paweltomaszpieta featurecenteredfirstorderstructuretensorscalespacein2dand3d AT andersbjorholmdahl featurecenteredfirstorderstructuretensorscalespacein2dand3d AT jepperevallfrisvad featurecenteredfirstorderstructuretensorscalespacein2dand3d AT siavasharjomandbigdeli featurecenteredfirstorderstructuretensorscalespacein2dand3d AT andersnymarkchristensen featurecenteredfirstorderstructuretensorscalespacein2dand3d |