ArticleSensors (Basel, Switzerland)2023
Colorimetric Characterization of Color Imaging System Based on Kernel Partial Least Squares.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Production of Special Fruit Beer With Addition of Cupuassu (Theobroma grandiflorum) Pulp and Prolyl Endopeptidase to Improve Volatile Compounds and Physicochemical Parameters.Journal of food science · 2026Article
- From Conventional to Craft Beer: Perception, Source, and Production of Beer Color-A Systematic Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2024Review
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Authors and funding
6 authors.
Funding
Abstract
Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy.
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