Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping
Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing produc...
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Veröffentlicht in: | IEEE transactions on automation science and engineering 2011-04, Vol.8 (2), p.292-302 |
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description | Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept. |
doi_str_mv | 10.1109/TASE.2010.2087325 |
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Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.</description><identifier>ISSN: 1545-5955</identifier><identifier>EISSN: 1558-3783</identifier><identifier>DOI: 10.1109/TASE.2010.2087325</identifier><identifier>CODEN: ITASC7</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Adjustment ; Applied sciences ; Artificial intelligence ; automatic color grading ; Automation ; Biological and medical sciences ; Calibration ; Color ; color space conversion ; Computer science; control theory; systems ; Conversion ; Evaluation ; Exact sciences and technology ; Food industries ; food processing ; Food quality ; fruit quality ; Fruits ; Fundamental and applied biological sciences. Psychology ; General aspects ; Gray-scale ; Image color analysis ; Indexes ; Industrial metrology. Testing ; Machine vision ; Mapping ; Mechanical engineering. Machine design ; Methods of analysis, processing and quality control, regulation, standards ; Pattern recognition. Digital image processing. Computational geometry ; Preferences ; Surface defects ; Vegetables ; visual inspection</subject><ispartof>IEEE transactions on automation science and engineering, 2011-04, Vol.8 (2), p.292-302</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.</description><subject>Adjustment</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>automatic color grading</subject><subject>Automation</subject><subject>Biological and medical sciences</subject><subject>Calibration</subject><subject>Color</subject><subject>color space conversion</subject><subject>Computer science; control theory; systems</subject><subject>Conversion</subject><subject>Evaluation</subject><subject>Exact sciences and technology</subject><subject>Food industries</subject><subject>food processing</subject><subject>Food quality</subject><subject>fruit quality</subject><subject>Fruits</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Gray-scale</subject><subject>Image color analysis</subject><subject>Indexes</subject><subject>Industrial metrology. Testing</subject><subject>Machine vision</subject><subject>Mapping</subject><subject>Mechanical engineering. Machine design</subject><subject>Methods of analysis, processing and quality control, regulation, standards</subject><subject>Pattern recognition. Digital image processing. 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Psychology</topic><topic>General aspects</topic><topic>Gray-scale</topic><topic>Image color analysis</topic><topic>Indexes</topic><topic>Industrial metrology. Testing</topic><topic>Machine vision</topic><topic>Mapping</topic><topic>Mechanical engineering. Machine design</topic><topic>Methods of analysis, processing and quality control, regulation, standards</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Preferences</topic><topic>Surface defects</topic><topic>Vegetables</topic><topic>visual inspection</topic><toplevel>online_resources</toplevel><creatorcontrib>Dah-Jye Lee</creatorcontrib><creatorcontrib>Archibald, J K</creatorcontrib><creatorcontrib>Guangming Xiong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on automation science and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dah-Jye Lee</au><au>Archibald, J K</au><au>Guangming Xiong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping</atitle><jtitle>IEEE transactions on automation science and engineering</jtitle><stitle>TASE</stitle><date>2011-04-01</date><risdate>2011</risdate><volume>8</volume><issue>2</issue><spage>292</spage><epage>302</epage><pages>292-302</pages><issn>1545-5955</issn><eissn>1558-3783</eissn><coden>ITASC7</coden><abstract>Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TASE.2010.2087325</doi><tpages>11</tpages></addata></record> |
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subjects | Adjustment Applied sciences Artificial intelligence automatic color grading Automation Biological and medical sciences Calibration Color color space conversion Computer science control theory systems Conversion Evaluation Exact sciences and technology Food industries food processing Food quality fruit quality Fruits Fundamental and applied biological sciences. Psychology General aspects Gray-scale Image color analysis Indexes Industrial metrology. Testing Machine vision Mapping Mechanical engineering. Machine design Methods of analysis, processing and quality control, regulation, standards Pattern recognition. Digital image processing. Computational geometry Preferences Surface defects Vegetables visual inspection |
title | Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping |
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