Online Measuring and Size Sorting for Perillae Based on Machine Vision
Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited b...
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| Veröffentlicht in: | Journal of sensors 2020, Vol.2020 (2020), p.1-8 |
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| creator | Jiang, Hanlu Dong, Xin Li, Yashuo Zhao, Rongqiang Fu, Jun Wang, Ye Zhao, Bo Lv, Chengxu |
| description | Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach. |
| doi_str_mv | 10.1155/2020/3125708 |
| format | Article |
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Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2020/3125708</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Calibration ; Error analysis ; Food ; Geometry ; Machine vision ; Mathematical models ; Parameter estimation ; Vision systems</subject><ispartof>Journal of sensors, 2020, Vol.2020 (2020), p.1-8</ispartof><rights>Copyright © 2020 Bo Zhao et al.</rights><rights>Copyright © 2020 Bo Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-876e218f6283aef9c25082a794bbcfd73b6fb9ccb61ce95f9580d231286263f73</citedby><cites>FETCH-LOGICAL-c427t-876e218f6283aef9c25082a794bbcfd73b6fb9ccb61ce95f9580d231286263f73</cites><orcidid>0000-0002-6810-6595 ; 0000-0003-0131-9542</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4022,27922,27923,27924</link.rule.ids></links><search><contributor>Li, Yuan</contributor><contributor>Yuan Li</contributor><creatorcontrib>Jiang, Hanlu</creatorcontrib><creatorcontrib>Dong, Xin</creatorcontrib><creatorcontrib>Li, Yashuo</creatorcontrib><creatorcontrib>Zhao, Rongqiang</creatorcontrib><creatorcontrib>Fu, Jun</creatorcontrib><creatorcontrib>Wang, Ye</creatorcontrib><creatorcontrib>Zhao, Bo</creatorcontrib><creatorcontrib>Lv, Chengxu</creatorcontrib><title>Online Measuring and Size Sorting for Perillae Based on Machine Vision</title><title>Journal of sensors</title><description>Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.</description><subject>Accuracy</subject><subject>Calibration</subject><subject>Error analysis</subject><subject>Food</subject><subject>Geometry</subject><subject>Machine vision</subject><subject>Mathematical models</subject><subject>Parameter estimation</subject><subject>Vision systems</subject><issn>1687-725X</issn><issn>1687-7268</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M9LwzAUB_AgCs7pzbMEPGpdfjQ_etSxqbAxYSreSpommlHTmWyI_vWmdOhRckgIn_ce7wvAKUZXGDM2IoigEcWECST3wABzKTJBuNz_fbOXQ3AU4wohTgWlAzBd-MZ5A-dGxW1w_hUqX8Ol-zZw2YZN92HbAB9McE2jDLxR0dSw9XCu9FtX-Oyia_0xOLCqieZkdw_B03TyOL7LZovb-_H1LNM5EZtMCm4IlpYTSZWxhSYMSaJEkVeVtrWgFbdVoXXFsTYFswWTqCZpI8kJp1bQITjv-65D-7E1cVOu2m3waWRJaM7SESxP6rJXOrQxBmPLdXDvKnyVGJVdUmWXVLlLKvGLnqeFavXp_tNnvTbJGKv-NC4QLQj9AQTqcGA</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Jiang, Hanlu</creator><creator>Dong, Xin</creator><creator>Li, Yashuo</creator><creator>Zhao, Rongqiang</creator><creator>Fu, Jun</creator><creator>Wang, Ye</creator><creator>Zhao, Bo</creator><creator>Lv, Chengxu</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>L7M</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-6810-6595</orcidid><orcidid>https://orcid.org/0000-0003-0131-9542</orcidid></search><sort><creationdate>2020</creationdate><title>Online Measuring and Size Sorting for Perillae Based on Machine Vision</title><author>Jiang, Hanlu ; 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Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. 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| subjects | Accuracy Calibration Error analysis Food Geometry Machine vision Mathematical models Parameter estimation Vision systems |
| title | Online Measuring and Size Sorting for Perillae Based on Machine Vision |
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