Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification
Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Karmouni, H. [verfasserIn] Yamni, M. [verfasserIn] El ogri, O. [verfasserIn] Daoui, A. [verfasserIn] Sayyouri, M. [verfasserIn] Qjidaa, H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
3D geometric invariant moments |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 |
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| Übergeordnetes Werk: |
volume:79 ; year:2020 ; number:39-40 ; day:08 ; month:08 ; pages:29121-29144 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-09351-1 |
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| Katalog-ID: |
SPR041297636 |
|---|
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| 245 | 1 | 0 | |a Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification |
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| 520 | |a Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. | ||
| 650 | 4 | |a 3D geometric invariant moments |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 3D Meixner invariant moments |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 3D image cuboid representation ( |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 3D image classification |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yamni, M. |e verfasserin |4 aut | |
| 700 | 1 | |a El ogri, O. |e verfasserin |4 aut | |
| 700 | 1 | |a Daoui, A. |e verfasserin |4 aut | |
| 700 | 1 | |a Sayyouri, M. |e verfasserin |4 aut | |
| 700 | 1 | |a Qjidaa, H. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
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10.1007/s11042-020-09351-1 doi (DE-627)SPR041297636 (SPR)s11042-020-09351-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Karmouni, H. verfasserin aut Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 Yamni, M. verfasserin aut El ogri, O. verfasserin aut Daoui, A. verfasserin aut Sayyouri, M. verfasserin aut Qjidaa, H. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 https://dx.doi.org/10.1007/s11042-020-09351-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 39-40 08 08 29121-29144 |
| spelling |
10.1007/s11042-020-09351-1 doi (DE-627)SPR041297636 (SPR)s11042-020-09351-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Karmouni, H. verfasserin aut Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 Yamni, M. verfasserin aut El ogri, O. verfasserin aut Daoui, A. verfasserin aut Sayyouri, M. verfasserin aut Qjidaa, H. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 https://dx.doi.org/10.1007/s11042-020-09351-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 39-40 08 08 29121-29144 |
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10.1007/s11042-020-09351-1 doi (DE-627)SPR041297636 (SPR)s11042-020-09351-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Karmouni, H. verfasserin aut Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 Yamni, M. verfasserin aut El ogri, O. verfasserin aut Daoui, A. verfasserin aut Sayyouri, M. verfasserin aut Qjidaa, H. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 https://dx.doi.org/10.1007/s11042-020-09351-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 39-40 08 08 29121-29144 |
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10.1007/s11042-020-09351-1 doi (DE-627)SPR041297636 (SPR)s11042-020-09351-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Karmouni, H. verfasserin aut Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 Yamni, M. verfasserin aut El ogri, O. verfasserin aut Daoui, A. verfasserin aut Sayyouri, M. verfasserin aut Qjidaa, H. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 https://dx.doi.org/10.1007/s11042-020-09351-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 39-40 08 08 29121-29144 |
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10.1007/s11042-020-09351-1 doi (DE-627)SPR041297636 (SPR)s11042-020-09351-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Karmouni, H. verfasserin aut Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 Yamni, M. verfasserin aut El ogri, O. verfasserin aut Daoui, A. verfasserin aut Sayyouri, M. verfasserin aut Qjidaa, H. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 https://dx.doi.org/10.1007/s11042-020-09351-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 39-40 08 08 29121-29144 |
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Enthalten in Multimedia tools and applications 79(2020), 39-40 vom: 08. Aug., Seite 29121-29144 volume:79 year:2020 number:39-40 day:08 month:08 pages:29121-29144 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR041297636</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024649.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201102s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-09351-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR041297636</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-09351-1-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="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Karmouni, H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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 In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">3D geometric invariant moments</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">3D Meixner invariant moments</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">3D image cuboid representation (</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">3D image classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yamni, M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">El ogri, O.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Daoui, A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sayyouri, M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qjidaa, H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">79(2020), 39-40 vom: 08. 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Karmouni, H. |
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Karmouni, H. ddc 070 bkl 54.87 misc 3D geometric invariant moments misc 3D Meixner invariant moments misc 3D image cuboid representation ( misc ) misc 3D image classification Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification |
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070 004 ASE 54.87 bkl Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification 3D geometric invariant moments (dpeaa)DE-He213 3D Meixner invariant moments (dpeaa)DE-He213 3D image cuboid representation ( (dpeaa)DE-He213 ) (dpeaa)DE-He213 3D image classification (dpeaa)DE-He213 |
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ddc 070 bkl 54.87 misc 3D geometric invariant moments misc 3D Meixner invariant moments misc 3D image cuboid representation ( misc ) misc 3D image classification |
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ddc 070 bkl 54.87 misc 3D geometric invariant moments misc 3D Meixner invariant moments misc 3D image cuboid representation ( misc ) misc 3D image classification |
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Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification |
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Multimedia tools and applications |
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Karmouni, H. Yamni, M. El ogri, O. Daoui, A. Sayyouri, M. Qjidaa, H. |
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fast computation of 3d meixner’s invariant moments using 3d image cuboid representation for 3d image classification |
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Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification |
| abstract |
Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. |
| abstractGer |
Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. |
| abstract_unstemmed |
Abstract In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental notions: the first is the extraction of the invariants of the 3D Meixner moments from the invariant of 3D geometric moments. The second is the use of 3D image cuboid representation (ICR). In this representation, the invariant moments of Meixner will be extracted from the cuboids instead of the overall image which allows reducing considerably the invariant computation time of 3D Meixner moments and consequently reducing the time of 3D image classification as well. In fact, the proposed method is tested by using several well-known computer vision data sets, including the moment invariability and the 3D image classification. Hence, the results obtained show the moments’ invariance extracted by the method proposed under the three different affine transformations: translation, scale and rotation of the 3D images, and clearly guarantee the efficiency of the proposed method in terms of calculation time and classification accuracy compared to existing methods. |
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| container_issue |
39-40 |
| title_short |
Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification |
| url |
https://dx.doi.org/10.1007/s11042-020-09351-1 |
| remote_bool |
true |
| author2 |
Yamni, M. El ogri, O. Daoui, A. Sayyouri, M. Qjidaa, H. |
| author2Str |
Yamni, M. El ogri, O. Daoui, A. Sayyouri, M. Qjidaa, H. |
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| doi_str |
10.1007/s11042-020-09351-1 |
| up_date |
2024-07-03T21:18:48.243Z |
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| score |
7.401457 |