Further development of adaptable automated visual inspection—part I: concept and scheme
Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspecti...
Ausführliche Beschreibung
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| Autor*in: |
Sun, Jun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2015 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 |
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| Übergeordnetes Werk: |
volume:81 ; year:2015 ; number:5-8 ; day:30 ; month:05 ; pages:1067-1076 |
| Links: |
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| DOI / URN: |
10.1007/s00170-015-7213-0 |
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SPR001872834 |
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| 520 | |a Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). | ||
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| 650 | 4 | |a Adaptability |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Classification |7 (dpeaa)DE-He213 | |
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10.1007/s00170-015-7213-0 doi (DE-627)SPR001872834 (SPR)s00170-015-7213-0-e DE-627 ger DE-627 rakwb eng Sun, Jun verfasserin aut Further development of adaptable automated visual inspection—part I: concept and scheme 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). Automated visual inspection (dpeaa)DE-He213 Adaptability (dpeaa)DE-He213 Online learning (dpeaa)DE-He213 Template matching (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Manufacturing (dpeaa)DE-He213 Machine vision (dpeaa)DE-He213 Sun, Qiao aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:30 month:05 pages:1067-1076 https://dx.doi.org/10.1007/s00170-015-7213-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_206 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_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 AR 81 2015 5-8 30 05 1067-1076 |
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10.1007/s00170-015-7213-0 doi (DE-627)SPR001872834 (SPR)s00170-015-7213-0-e DE-627 ger DE-627 rakwb eng Sun, Jun verfasserin aut Further development of adaptable automated visual inspection—part I: concept and scheme 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). Automated visual inspection (dpeaa)DE-He213 Adaptability (dpeaa)DE-He213 Online learning (dpeaa)DE-He213 Template matching (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Manufacturing (dpeaa)DE-He213 Machine vision (dpeaa)DE-He213 Sun, Qiao aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:30 month:05 pages:1067-1076 https://dx.doi.org/10.1007/s00170-015-7213-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_206 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_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 AR 81 2015 5-8 30 05 1067-1076 |
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10.1007/s00170-015-7213-0 doi (DE-627)SPR001872834 (SPR)s00170-015-7213-0-e DE-627 ger DE-627 rakwb eng Sun, Jun verfasserin aut Further development of adaptable automated visual inspection—part I: concept and scheme 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). Automated visual inspection (dpeaa)DE-He213 Adaptability (dpeaa)DE-He213 Online learning (dpeaa)DE-He213 Template matching (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Manufacturing (dpeaa)DE-He213 Machine vision (dpeaa)DE-He213 Sun, Qiao aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:30 month:05 pages:1067-1076 https://dx.doi.org/10.1007/s00170-015-7213-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_206 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_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 AR 81 2015 5-8 30 05 1067-1076 |
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10.1007/s00170-015-7213-0 doi (DE-627)SPR001872834 (SPR)s00170-015-7213-0-e DE-627 ger DE-627 rakwb eng Sun, Jun verfasserin aut Further development of adaptable automated visual inspection—part I: concept and scheme 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). Automated visual inspection (dpeaa)DE-He213 Adaptability (dpeaa)DE-He213 Online learning (dpeaa)DE-He213 Template matching (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Manufacturing (dpeaa)DE-He213 Machine vision (dpeaa)DE-He213 Sun, Qiao aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:30 month:05 pages:1067-1076 https://dx.doi.org/10.1007/s00170-015-7213-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_206 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_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 AR 81 2015 5-8 30 05 1067-1076 |
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10.1007/s00170-015-7213-0 doi (DE-627)SPR001872834 (SPR)s00170-015-7213-0-e DE-627 ger DE-627 rakwb eng Sun, Jun verfasserin aut Further development of adaptable automated visual inspection—part I: concept and scheme 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). Automated visual inspection (dpeaa)DE-He213 Adaptability (dpeaa)DE-He213 Online learning (dpeaa)DE-He213 Template matching (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Manufacturing (dpeaa)DE-He213 Machine vision (dpeaa)DE-He213 Sun, Qiao aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 30. Mai, Seite 1067-1076 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:30 month:05 pages:1067-1076 https://dx.doi.org/10.1007/s00170-015-7213-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_206 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_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 AR 81 2015 5-8 30 05 1067-1076 |
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The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. 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further development of adaptable automated visual inspection—part i: concept and scheme |
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Further development of adaptable automated visual inspection—part I: concept and scheme |
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Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). © Springer-Verlag London 2015 |
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Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). © Springer-Verlag London 2015 |
| abstract_unstemmed |
Abstract The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely $ A^{2} $VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of $ A^{2} $VIS in a systemic perspective. In part I, the proposed $ A^{2} $VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the $ A^{2} $VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of $ A^{2} $VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM). © Springer-Verlag London 2015 |
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Further development of adaptable automated visual inspection—part I: concept and scheme |
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Sun, Qiao |
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| score |
7.3979445 |