Directional fractal signature analysis of self-structured surface textures
Currently available directional fractal signature (DFS) methods are not suited for self-structured surface textures since they base on the assumption of Brownian fractal or they do not use the entire image data in calculation. To address these difficulties, two new DFS methods were developed in this...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Springer
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/28119 |
| _version_ | 1848752449950580736 |
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| author | Wolski, Marcin Podsiadlo, Pawel Stachowiak, Gwidon |
| author_facet | Wolski, Marcin Podsiadlo, Pawel Stachowiak, Gwidon |
| author_sort | Wolski, Marcin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Currently available directional fractal signature (DFS) methods are not suited for self-structured surface textures since they base on the assumption of Brownian fractal or they do not use the entire image data in calculation. To address these difficulties, two new DFS methods were developed in this study, i.e., an augmented blanket with rotating grid (ABRG) method and a blanket with shearing image (BSI) method. The performance of these methods in measuring surface roughness and directionality, the capacity for quantifying multi-patterned textures, and the ability to detect differences between textures of self-structured surfaces were evaluated. The methods were compared against a blanket with rotating grid (BRG) method. Computer-generated images of self-structured surface textures with different roughness, directions and patterns, and atomic force microscope images of real self-structured surfaces were used. The computer texture images were generated using a specially developed motif-based texture generator. Results obtained showed that the ABRG method is more accurate and reliable than the BRG and BSI methods. |
| first_indexed | 2025-11-14T08:08:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-28119 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:08:48Z |
| publishDate | 2012 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-281192017-09-13T15:14:32Z Directional fractal signature analysis of self-structured surface textures Wolski, Marcin Podsiadlo, Pawel Stachowiak, Gwidon Numerical analysis Self-structured surfaces Surface characterization Texture Currently available directional fractal signature (DFS) methods are not suited for self-structured surface textures since they base on the assumption of Brownian fractal or they do not use the entire image data in calculation. To address these difficulties, two new DFS methods were developed in this study, i.e., an augmented blanket with rotating grid (ABRG) method and a blanket with shearing image (BSI) method. The performance of these methods in measuring surface roughness and directionality, the capacity for quantifying multi-patterned textures, and the ability to detect differences between textures of self-structured surfaces were evaluated. The methods were compared against a blanket with rotating grid (BRG) method. Computer-generated images of self-structured surface textures with different roughness, directions and patterns, and atomic force microscope images of real self-structured surfaces were used. The computer texture images were generated using a specially developed motif-based texture generator. Results obtained showed that the ABRG method is more accurate and reliable than the BRG and BSI methods. 2012 Journal Article http://hdl.handle.net/20.500.11937/28119 10.1007/s11249-012-9988-6 Springer restricted |
| spellingShingle | Numerical analysis Self-structured surfaces Surface characterization Texture Wolski, Marcin Podsiadlo, Pawel Stachowiak, Gwidon Directional fractal signature analysis of self-structured surface textures |
| title | Directional fractal signature analysis of self-structured surface textures |
| title_full | Directional fractal signature analysis of self-structured surface textures |
| title_fullStr | Directional fractal signature analysis of self-structured surface textures |
| title_full_unstemmed | Directional fractal signature analysis of self-structured surface textures |
| title_short | Directional fractal signature analysis of self-structured surface textures |
| title_sort | directional fractal signature analysis of self-structured surface textures |
| topic | Numerical analysis Self-structured surfaces Surface characterization Texture |
| url | http://hdl.handle.net/20.500.11937/28119 |