Explaining Color Evolution, Color Blindness, and Color Recognition by the Decoding Model of Color Vision - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2020

Explaining Color Evolution, Color Blindness, and Color Recognition by the Decoding Model of Color Vision

Chenguang Lu
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Résumé

The author proposed the decoding model of color vision in 1987. International Commission on Illumination (CIE) recommended almost the same symmetric color model for color transform in 2006. For readers to understand the decoding model better, this paper first introduces the decoding model, then uses this model to explain the opponent-process, color evolution, and color blindness pictorially. Recent references on the decoding and reconstruction of colors in the visual cortex induce a new question: what is the decoding algorithm from ganglion cells to the visual cortex? This paper also explains the decoding algorithm. The decoding model is explained as a fuzzy 3–8 decoder. The fuzzy logic used is compatible with Boolean Algebra. The model first obtains the median M of three cones’ outputs B, G, and R, and then obtain three opponent signals by B, G, and R minus M respectively. This model can unify Young and Helmholtz’s tri-pigment theory and Hering’s opponent theory more naturally than the popular zone models. It is symmetrical and compatible with the popular color transform method for computer graphics. The transform from the RGB system to HSV system according to the decoding model is introduced. Several fuzzy 3–8 or 4–16 … fuzzy decoders can be used to construct a Decoding Neural Network (DNN). The decoding algorithm from ganglion cells to the visual cortex can be explained with a two-layer DNN. The reasonability of the decoding model and the potential applications of the DNN are discussed.
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hal-03456975 , version 1 (30-11-2021)

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Chenguang Lu. Explaining Color Evolution, Color Blindness, and Color Recognition by the Decoding Model of Color Vision. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.287-298, ⟨10.1007/978-3-030-46931-3_27⟩. ⟨hal-03456975⟩
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