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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Jelmer Philip de Vries¹, Alban Flachot², Takuma Morimoto^1,3

¹Department of Psychology, Justus Liebig Universitat, Giessen, Germany vriesdejelmer@gmail.com; gegenfurtner@uni-giessen.de www.jelmerdevries.com; https://www.allpsych.uni-giessen.de/karl/.

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Summary

Deep neural networks (DNNs) are challenged as models for human vision. This study argues DNNs are viable, addressing misconceptions about color perception and model development in visual science research.

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Visualizing Visual Adaptation

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Area of Science:

Cognitive Science
Computer Vision
Neuroscience

Background:

Deep neural networks (DNNs) are increasingly used to model human visual perception.
Bowers et al. recently questioned the efficacy of DNNs in this capacity.
This critique may stem from misunderstandings of current DNN capabilities and research directions.

Purpose of the Study:

To counter the claims made by Bowers et al. regarding DNNs as models of human visual perception.
To clarify the current state-of-the-art in color perception research.
To highlight the advancements and future potential of DNNs in understanding visual processing.

Main Methods:

Analysis of existing literature on color perception and DNNs.
Critique of the specific arguments presented by Bowers et al.
Comparison of current DNN architectures with human visual system requirements.

Main Results:

The critique by Bowers et al. is based on three key misconceptions.
These misconceptions involve the state-of-the-art in color perception, the necessary model types for progress, and the resolution of existing DNN limitations.
DNNs represent a promising avenue for modeling human visual perception, particularly in color processing.

Conclusions:

Deep neural networks are not adequately represented by Bowers et al.'s critique.
Further research should focus on the advancements in DNNs for understanding human visual perception.
The field requires models that accurately reflect the complexities of both biological and artificial vision systems.