A Neural Network Approach to Estimating Color Reflectance with Product Independent Models
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Akanuma, Asei and Stamate, Daniel. 2022. 'A Neural Network Approach to Estimating Color Reflectance with Product Independent Models'. In: 31st International Conference on Artificial Neural Network. Bristol, United Kingdom 6 - 9 September 2022. [Conference or Workshop Item]
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Abstract or Description
In the paint and coatings industry, traditionally color reflectance modelling is performed individually for each coating product. This is because a coating product contains color samples that are mixed from several colorants and a binder which have a unique chemical property that requires modelling to be carried out individually when done analytically. This work proposes a superior approach for color reflectance modelling based on Neural Networks, which is capable of modelling multiple coating products concurrently using a single model, allowing for a modelling approach that is generic and independent of the coating products. In this study we demonstrate that our Neural Network model optimized to predict color reflectance for multiple coating products using a dataset with 4150 color samples containing 18 distinct coating products, is able to perform better (RMSE 3.73) than an widely employed analytical model, Kubelka-Munk (RMSE 8.24), which is conventionally used for the same task.
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Item Type: |
Conference or Workshop Item (Paper) |
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| Additional Information: |
“This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-15934-3_66. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms”. |
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| Keywords: |
Prediction modelling, Neural networks, Color, Paints, Reflectance |
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| Dates: |
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| Event Location: |
Bristol, United Kingdom |
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| Date range: |
6 - 9 September 2022 |
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| Item ID: |
32816 |
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| Date Deposited: |
20 Dec 2022 11:52 |
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| Last Modified: |
15 Sep 2023 01:26 |
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