New graph-based features for shape recognition
Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destructi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mirehi, Narges [verfasserIn] Tahmasbi, Maryam [verfasserIn] Targhi, Alireza Tavakoli [verfasserIn] |
|---|
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E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2021), 11 vom: 20. März, Seite 7577-7592 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:11 ; day:20 ; month:03 ; pages:7577-7592 |
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| DOI / URN: |
10.1007/s00500-021-05716-2 |
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SPR04404335X |
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| 520 | |a Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. | ||
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10.1007/s00500-021-05716-2 doi (DE-627)SPR04404335X (DE-599)SPRs00500-021-05716-2-e (SPR)s00500-021-05716-2-e DE-627 ger DE-627 rakwb eng Mirehi, Narges verfasserin aut New graph-based features for shape recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. Shape recognition (dpeaa)DE-He213 GNG graph (dpeaa)DE-He213 Graph distance (dpeaa)DE-He213 Graph-based features (dpeaa)DE-He213 Tahmasbi, Maryam verfasserin aut Targhi, Alireza Tavakoli verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 11 vom: 20. März, Seite 7577-7592 (DE-627)SPR006469531 nnns volume:25 year:2021 number:11 day:20 month:03 pages:7577-7592 https://dx.doi.org/10.1007/s00500-021-05716-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 11 20 03 7577-7592 |
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10.1007/s00500-021-05716-2 doi (DE-627)SPR04404335X (DE-599)SPRs00500-021-05716-2-e (SPR)s00500-021-05716-2-e DE-627 ger DE-627 rakwb eng Mirehi, Narges verfasserin aut New graph-based features for shape recognition 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. Shape recognition (dpeaa)DE-He213 GNG graph (dpeaa)DE-He213 Graph distance (dpeaa)DE-He213 Graph-based features (dpeaa)DE-He213 Tahmasbi, Maryam verfasserin aut Targhi, Alireza Tavakoli verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 11 vom: 20. März, Seite 7577-7592 (DE-627)SPR006469531 nnns volume:25 year:2021 number:11 day:20 month:03 pages:7577-7592 https://dx.doi.org/10.1007/s00500-021-05716-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 11 20 03 7577-7592 |
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Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. |
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Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. |
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Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR04404335X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210516064741.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210516s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-021-05716-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04404335X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs00500-021-05716-2-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-021-05716-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mirehi, Narges</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">New graph-based features for shape recognition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. 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