Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm
The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/6/1212 |
| _version_ | 1797592562361434112 |
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| author | Borut Žalik Damjan Strnad David Podgorelec Ivana Kolingerová Andrej Nerat Niko Lukač Štefan Kohek Luka Lukač |
| author_facet | Borut Žalik Damjan Strnad David Podgorelec Ivana Kolingerová Andrej Nerat Niko Lukač Štefan Kohek Luka Lukač |
| author_sort | Borut Žalik |
| collection | DOAJ |
| description | The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape’s points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape’s characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes. |
| first_indexed | 2024-03-11T01:52:59Z |
| format | Article |
| id | doaj.art-4ec94b895ca144439e3869b5dc6d08fd |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T01:52:59Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-4ec94b895ca144439e3869b5dc6d08fd2023-11-18T12:51:01ZengMDPI AGSymmetry2073-89942023-06-01156121210.3390/sym15061212Geometric Shape Characterisation Based on a Multi-Sweeping ParadigmBorut Žalik 0Damjan Strnad 1David Podgorelec 2Ivana Kolingerová 3Andrej Nerat 4Niko Lukač 5Štefan Kohek 6Luka Lukač 7Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaDepartment of Computer Science and Engineering, University of West Bohemia, Technická 8, 306 14 Plzeň, Czech RepublicFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, SloveniaThe characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape’s points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape’s characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes.https://www.mdpi.com/2073-8994/15/6/1212computer scienceimage analysiscomputational geometrylocal reflection symmetry |
| spellingShingle | Borut Žalik Damjan Strnad David Podgorelec Ivana Kolingerová Andrej Nerat Niko Lukač Štefan Kohek Luka Lukač Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm Symmetry computer science image analysis computational geometry local reflection symmetry |
| title | Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm |
| title_full | Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm |
| title_fullStr | Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm |
| title_full_unstemmed | Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm |
| title_short | Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm |
| title_sort | geometric shape characterisation based on a multi sweeping paradigm |
| topic | computer science image analysis computational geometry local reflection symmetry |
| url | https://www.mdpi.com/2073-8994/15/6/1212 |
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