Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation
Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Umamaheswara Raju, R. S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© The Optical Society of India 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of optics - [New Delhi] : Springer India, 1972, 47(2018), 2 vom: 08. März, Seite 243-250 |
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| Übergeordnetes Werk: |
volume:47 ; year:2018 ; number:2 ; day:08 ; month:03 ; pages:243-250 |
| Links: |
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| DOI / URN: |
10.1007/s12596-018-0457-y |
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SPR026233533 |
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| 520 | |a Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. | ||
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| 700 | 1 | |a Raju, V. Ramachandra |4 aut | |
| 700 | 1 | |a Mohammad, Sharfuddin |4 aut | |
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10.1007/s12596-018-0457-y doi (DE-627)SPR026233533 (SPR)s12596-018-0457-y-e DE-627 ger DE-627 rakwb eng Umamaheswara Raju, R. S. verfasserin aut Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Optical Society of India 2018 Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. Machine vision (dpeaa)DE-He213 Intelligent system (dpeaa)DE-He213 Curvelet transforms (dpeaa)DE-He213 FPA (dpeaa)DE-He213 Ramesh, R. aut Raju, V. Ramachandra aut Mohammad, Sharfuddin aut Enthalten in Journal of optics [New Delhi] : Springer India, 1972 47(2018), 2 vom: 08. März, Seite 243-250 (DE-627)616732775 (DE-600)2533862-6 0974-6900 nnns volume:47 year:2018 number:2 day:08 month:03 pages:243-250 https://dx.doi.org/10.1007/s12596-018-0457-y 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_65 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_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 AR 47 2018 2 08 03 243-250 |
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10.1007/s12596-018-0457-y doi (DE-627)SPR026233533 (SPR)s12596-018-0457-y-e DE-627 ger DE-627 rakwb eng Umamaheswara Raju, R. S. verfasserin aut Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Optical Society of India 2018 Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. Machine vision (dpeaa)DE-He213 Intelligent system (dpeaa)DE-He213 Curvelet transforms (dpeaa)DE-He213 FPA (dpeaa)DE-He213 Ramesh, R. aut Raju, V. Ramachandra aut Mohammad, Sharfuddin aut Enthalten in Journal of optics [New Delhi] : Springer India, 1972 47(2018), 2 vom: 08. März, Seite 243-250 (DE-627)616732775 (DE-600)2533862-6 0974-6900 nnns volume:47 year:2018 number:2 day:08 month:03 pages:243-250 https://dx.doi.org/10.1007/s12596-018-0457-y 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_65 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_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 AR 47 2018 2 08 03 243-250 |
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10.1007/s12596-018-0457-y doi (DE-627)SPR026233533 (SPR)s12596-018-0457-y-e DE-627 ger DE-627 rakwb eng Umamaheswara Raju, R. S. verfasserin aut Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Optical Society of India 2018 Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. Machine vision (dpeaa)DE-He213 Intelligent system (dpeaa)DE-He213 Curvelet transforms (dpeaa)DE-He213 FPA (dpeaa)DE-He213 Ramesh, R. aut Raju, V. Ramachandra aut Mohammad, Sharfuddin aut Enthalten in Journal of optics [New Delhi] : Springer India, 1972 47(2018), 2 vom: 08. März, Seite 243-250 (DE-627)616732775 (DE-600)2533862-6 0974-6900 nnns volume:47 year:2018 number:2 day:08 month:03 pages:243-250 https://dx.doi.org/10.1007/s12596-018-0457-y 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_65 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_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 AR 47 2018 2 08 03 243-250 |
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10.1007/s12596-018-0457-y doi (DE-627)SPR026233533 (SPR)s12596-018-0457-y-e DE-627 ger DE-627 rakwb eng Umamaheswara Raju, R. S. verfasserin aut Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Optical Society of India 2018 Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. Machine vision (dpeaa)DE-He213 Intelligent system (dpeaa)DE-He213 Curvelet transforms (dpeaa)DE-He213 FPA (dpeaa)DE-He213 Ramesh, R. aut Raju, V. Ramachandra aut Mohammad, Sharfuddin aut Enthalten in Journal of optics [New Delhi] : Springer India, 1972 47(2018), 2 vom: 08. März, Seite 243-250 (DE-627)616732775 (DE-600)2533862-6 0974-6900 nnns volume:47 year:2018 number:2 day:08 month:03 pages:243-250 https://dx.doi.org/10.1007/s12596-018-0457-y 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_65 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_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 AR 47 2018 2 08 03 243-250 |
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Umamaheswara Raju, R. S. |
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Umamaheswara Raju, R. S. misc Machine vision misc Intelligent system misc Curvelet transforms misc FPA Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation |
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Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation Machine vision (dpeaa)DE-He213 Intelligent system (dpeaa)DE-He213 Curvelet transforms (dpeaa)DE-He213 FPA (dpeaa)DE-He213 |
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Umamaheswara Raju, R. S. Ramesh, R. Raju, V. Ramachandra Mohammad, Sharfuddin |
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curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation |
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Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation |
| abstract |
Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. © The Optical Society of India 2018 |
| abstractGer |
Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. © The Optical Society of India 2018 |
| abstract_unstemmed |
Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model. © The Optical Society of India 2018 |
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Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation |
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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">SPR026233533</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331232746.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12596-018-0457-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026233533</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12596-018-0457-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Umamaheswara Raju, R. S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="500" ind1=" " ind2=" "><subfield code="a">© The Optical Society of India 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine vision</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligent system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Curvelet transforms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FPA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramesh, R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Raju, V. 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