Abstract
We present in this paper the application of a fuzzy logic inference system to characterize liquid-crystal displays. We use the so-called fuzzy modelling and identification toolbox (FMID, Mathworks) to build a fuzzy logic inference system from a set of input and output data. The advantage of building a model like this, aside from its good performance, relies on its interpretability. Once trained, we obtain a physical interpretation of the model. We use training and testing datasets relating device dependent RGB data with device independent XYZ or xyY coordinates, measured with a colorimeter. We study different configurations for the model and compare them with three state-of-the-art methods in terms of \(\varDelta E00\) visual error. This study is restricted to a single display and therefore we also point out what features of the learned model we think are more display dependent and might possibly change for a different display.
Supported by Generalitat Valenciana under AICO 2023 program.
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Almutairi, K., Morillas, S., Latorre-Carmona, P., Dansoko, M. (2023). A Fuzzy Logic Inference System for Display Characterization. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_5
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