A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI
Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cheng, Yuhua [verfasserIn] Tian, Lulu [verfasserIn] Yin, Chun [verfasserIn] Huang, Xuegang [verfasserIn] Bai, Libing [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Nonlinear dynamics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 86(2016), 2 vom: 29. Juni, Seite 741-750 |
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| Übergeordnetes Werk: |
volume:86 ; year:2016 ; number:2 ; day:29 ; month:06 ; pages:741-750 |
| Links: |
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| DOI / URN: |
10.1007/s11071-016-2918-7 |
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| Katalog-ID: |
SPR016388224 |
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| 100 | 1 | |a Cheng, Yuhua |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI |
| 264 | 1 | |c 2016 | |
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| 520 | |a Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. | ||
| 650 | 4 | |a Magnetic optic imaging (MOI) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Threshold searching |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Magnetic domain |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crack detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image processing |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tian, Lulu |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xuegang |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Libing |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nonlinear dynamics |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 |g 86(2016), 2 vom: 29. Juni, Seite 741-750 |w (DE-627)315297034 |w (DE-600)2012600-1 |x 1573-269X |7 nnns |
| 773 | 1 | 8 | |g volume:86 |g year:2016 |g number:2 |g day:29 |g month:06 |g pages:741-750 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11071-016-2918-7 |z lizenzpflichtig |3 Volltext |
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10.1007/s11071-016-2918-7 doi (DE-627)SPR016388224 (SPR)s11071-016-2918-7-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Cheng, Yuhua verfasserin aut A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Tian, Lulu verfasserin aut Yin, Chun verfasserin aut Huang, Xuegang verfasserin aut Bai, Libing verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 86(2016), 2 vom: 29. Juni, Seite 741-750 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:86 year:2016 number:2 day:29 month:06 pages:741-750 https://dx.doi.org/10.1007/s11071-016-2918-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 86 2016 2 29 06 741-750 |
| spelling |
10.1007/s11071-016-2918-7 doi (DE-627)SPR016388224 (SPR)s11071-016-2918-7-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Cheng, Yuhua verfasserin aut A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Tian, Lulu verfasserin aut Yin, Chun verfasserin aut Huang, Xuegang verfasserin aut Bai, Libing verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 86(2016), 2 vom: 29. Juni, Seite 741-750 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:86 year:2016 number:2 day:29 month:06 pages:741-750 https://dx.doi.org/10.1007/s11071-016-2918-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 86 2016 2 29 06 741-750 |
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10.1007/s11071-016-2918-7 doi (DE-627)SPR016388224 (SPR)s11071-016-2918-7-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Cheng, Yuhua verfasserin aut A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Tian, Lulu verfasserin aut Yin, Chun verfasserin aut Huang, Xuegang verfasserin aut Bai, Libing verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 86(2016), 2 vom: 29. Juni, Seite 741-750 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:86 year:2016 number:2 day:29 month:06 pages:741-750 https://dx.doi.org/10.1007/s11071-016-2918-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 86 2016 2 29 06 741-750 |
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10.1007/s11071-016-2918-7 doi (DE-627)SPR016388224 (SPR)s11071-016-2918-7-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Cheng, Yuhua verfasserin aut A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Tian, Lulu verfasserin aut Yin, Chun verfasserin aut Huang, Xuegang verfasserin aut Bai, Libing verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 86(2016), 2 vom: 29. Juni, Seite 741-750 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:86 year:2016 number:2 day:29 month:06 pages:741-750 https://dx.doi.org/10.1007/s11071-016-2918-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 86 2016 2 29 06 741-750 |
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10.1007/s11071-016-2918-7 doi (DE-627)SPR016388224 (SPR)s11071-016-2918-7-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Cheng, Yuhua verfasserin aut A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Tian, Lulu verfasserin aut Yin, Chun verfasserin aut Huang, Xuegang verfasserin aut Bai, Libing verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 86(2016), 2 vom: 29. Juni, Seite 741-750 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:86 year:2016 number:2 day:29 month:06 pages:741-750 https://dx.doi.org/10.1007/s11071-016-2918-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 86 2016 2 29 06 741-750 |
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English |
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Enthalten in Nonlinear dynamics 86(2016), 2 vom: 29. Juni, Seite 741-750 volume:86 year:2016 number:2 day:29 month:06 pages:741-750 |
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Enthalten in Nonlinear dynamics 86(2016), 2 vom: 29. Juni, Seite 741-750 volume:86 year:2016 number:2 day:29 month:06 pages:741-750 |
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Magnetic optic imaging (MOI) Threshold searching Magnetic domain Crack detection Image processing |
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Nonlinear dynamics |
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Cheng, Yuhua @@aut@@ Tian, Lulu @@aut@@ Yin, Chun @@aut@@ Huang, Xuegang @@aut@@ Bai, Libing @@aut@@ |
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2016-06-29T00:00:00Z |
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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">SPR016388224</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111031503.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11071-016-2918-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR016388224</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11071-016-2918-7-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">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">30.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cheng, Yuhua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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 The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. 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Cheng, Yuhua |
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Cheng, Yuhua ddc 510 bkl 30.20 misc Magnetic optic imaging (MOI) misc Threshold searching misc Magnetic domain misc Crack detection misc Image processing A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI |
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510 ASE 30.20 bkl A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI Magnetic optic imaging (MOI) (dpeaa)DE-He213 Threshold searching (dpeaa)DE-He213 Magnetic domain (dpeaa)DE-He213 Crack detection (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 |
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ddc 510 bkl 30.20 misc Magnetic optic imaging (MOI) misc Threshold searching misc Magnetic domain misc Crack detection misc Image processing |
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ddc 510 bkl 30.20 misc Magnetic optic imaging (MOI) misc Threshold searching misc Magnetic domain misc Crack detection misc Image processing |
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A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI |
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Cheng, Yuhua Tian, Lulu Yin, Chun Huang, Xuegang Bai, Libing |
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magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the moi |
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A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI |
| abstract |
Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. |
| abstractGer |
Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. |
| abstract_unstemmed |
Abstract The paper proposes a method for crack detection using the magnetic optic image. It presents a way to filter the magnetic domain spots based on the image segmentation method utilizing the connectivity principle of image. As the area of the crack is much bigger than the magnetic domain spots and there is a mutation in the point, whose normalized slope is beyond %$45^\circ %$, between the crack and the magnetic domain spots after sorting the area value of them, the machinery could filter the magnetic domain spots. In order to filter the spots, how to select the threshold is very important. The paper presents a self-adapting selection method to choose the threshold which based the pixel filling model. There would be a fast changing point of the filling area curve when the area increases. There is a corresponding filling value that is considered as the good threshold. Based on the features, the detecting system can recognize the crack clearly and the results would confirmed it. |
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| container_issue |
2 |
| title_short |
A magnetic domain spots filtering method with self-adapting threshold value selecting for crack detection based on the MOI |
| url |
https://dx.doi.org/10.1007/s11071-016-2918-7 |
| remote_bool |
true |
| author2 |
Tian, Lulu Yin, Chun Huang, Xuegang Bai, Libing |
| author2Str |
Tian, Lulu Yin, Chun Huang, Xuegang Bai, Libing |
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| doi_str |
10.1007/s11071-016-2918-7 |
| up_date |
2024-07-03T22:47:01.634Z |
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| score |
7.4000454 |