Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization
We describe color reproduction and correction of images captured by electronic cameras under multiple illumination (or lighting) conditions, relating to color device characterization for enhancing the quality of color in the obtained images. In particular, we highlight a very practical use of neuro-...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2002-07, Vol.13 (4), p.1009-1022 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Mizutani, E. Nishio, K. |
| description | We describe color reproduction and correction of images captured by electronic cameras under multiple illumination (or lighting) conditions, relating to color device characterization for enhancing the quality of color in the obtained images. In particular, we highlight a very practical use of neuro-fuzzy modular network coactive neuro-fuzzy inference systems (CANFIS) models for this application, and discuss their strengths and weaknesses compared with other adaptive network models (e.g., multilayer perceptron (MLP)) as well as conventional lookup-table-type (TRC-matrix) methods. Our in-depth investigation based on comprehensive numerical tests with a wide variety of illumination/lighting data (180 sources of illumination) shows that the "neuro-fuzzy CANFIS with MLP local experts" possesses a remarkable generalization/approximation capacity, even under a very restricted condition where only four-illuminant data sets were permitted to be used for optimization because of efficient practical implementation subject to an industrial setting. |
| doi_str_mv | 10.1109/TNN.2002.1021900 |
| format | Article |
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In particular, we highlight a very practical use of neuro-fuzzy modular network coactive neuro-fuzzy inference systems (CANFIS) models for this application, and discuss their strengths and weaknesses compared with other adaptive network models (e.g., multilayer perceptron (MLP)) as well as conventional lookup-table-type (TRC-matrix) methods. Our in-depth investigation based on comprehensive numerical tests with a wide variety of illumination/lighting data (180 sources of illumination) shows that the "neuro-fuzzy CANFIS with MLP local experts" possesses a remarkable generalization/approximation capacity, even under a very restricted condition where only four-illuminant data sets were permitted to be used for optimization because of efficient practical implementation subject to an industrial setting.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNN.2002.1021900</identifier><identifier>PMID: 18244495</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptive systems ; Artificial neural networks ; Cameras ; Color ; Electronics ; Fuzzy logic ; Humans ; Illumination ; Image converters ; Layout ; Lighting ; Mathematical models ; Multilayer perceptrons ; Networks ; Printers ; Testing</subject><ispartof>IEEE transaction on neural networks and learning systems, 2002-07, Vol.13 (4), p.1009-1022</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2002</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c437t-bd4b871b48a4fe22041ea89079e14a075804a829cf67ad0f36cd576dc2ca76463</citedby><cites>FETCH-LOGICAL-c437t-bd4b871b48a4fe22041ea89079e14a075804a829cf67ad0f36cd576dc2ca76463</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1021900$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1021900$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18244495$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mizutani, E.</creatorcontrib><creatorcontrib>Nishio, K.</creatorcontrib><title>Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>We describe color reproduction and correction of images captured by electronic cameras under multiple illumination (or lighting) conditions, relating to color device characterization for enhancing the quality of color in the obtained images. In particular, we highlight a very practical use of neuro-fuzzy modular network coactive neuro-fuzzy inference systems (CANFIS) models for this application, and discuss their strengths and weaknesses compared with other adaptive network models (e.g., multilayer perceptron (MLP)) as well as conventional lookup-table-type (TRC-matrix) methods. Our in-depth investigation based on comprehensive numerical tests with a wide variety of illumination/lighting data (180 sources of illumination) shows that the "neuro-fuzzy CANFIS with MLP local experts" possesses a remarkable generalization/approximation capacity, even under a very restricted condition where only four-illuminant data sets were permitted to be used for optimization because of efficient practical implementation subject to an industrial setting.</description><subject>Adaptive systems</subject><subject>Artificial neural networks</subject><subject>Cameras</subject><subject>Color</subject><subject>Electronics</subject><subject>Fuzzy logic</subject><subject>Humans</subject><subject>Illumination</subject><subject>Image converters</subject><subject>Layout</subject><subject>Lighting</subject><subject>Mathematical models</subject><subject>Multilayer perceptrons</subject><subject>Networks</subject><subject>Printers</subject><subject>Testing</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFks9rFDEUxwdRbK3eBUFCD3qa9SWTn8eyWC2068F6DtnMG0zNTGpmprL715t1F1o8tKcXHp_vB174VtVbCgtKwXy6Xq0WDIAtKDBqAJ5Vx9RwWgOY5nl5Axe1YUwdVa_G8QaAcgHyZXVENeOcG3Fcba7mOIU6xDj3YXDDRHyKKZOMtzm1s59CGkhXFhjRTzkNwRPvesxuJHfBkeXZ6vziOxlwzqnu5u12Q_qSiy6X3fQn5V-kxbvgkfifLjs_YQ5bt7O-rl50Lo745jBPqh_nn6-XX-vLb18ulmeXteeNmup1y9da0TXXjnfIGHCKThtQBil3oIQG7jQzvpPKtdA10rdCydYz75TksjmpPu695aDfM46T7cPoMUY3YJpHa2SjpaaUPkmqhjOu1T_nh0dJphvFheBPg4pRCUIU8PQ_8CbNeSgfYw0DLU1jdjbYQz6ncczY2dscepc3loLdFcKWQthdIeyhECXy_uCd1z2294FDAwrwbg8ERHzg28f_AmGjugg</recordid><startdate>20020701</startdate><enddate>20020701</enddate><creator>Mizutani, E.</creator><creator>Nishio, K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptive systems Artificial neural networks Cameras Color Electronics Fuzzy logic Humans Illumination Image converters Layout Lighting Mathematical models Multilayer perceptrons Networks Printers Testing |
| title | Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization |
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