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A Machine Learning Model Perceiving Brightness Optical Illusions: Quantitative Evaluation with Psychophysical Data

Published: 11 July 2021 Publication History

Abstract

Creating machine learning models that perceive optical illusions is essential to discover human perceptual bias for developing human augmentation technology. This study proposes a machine learning model perceiving brightness optical illusions and quantitative comparison methods of model output with psychophysical data. After considering handling methods of two psychophysical quantities, brightness and spatial frequency, we compared the output of convolutional neural networks (CNNs) that trained three types of tasks with two psychophysical data for Munker–White illusions depending on spatial frequency. Consequently, the three models reproduced perceptual characteristics corresponding to each learning task. Additionally, an optimal parameter was obtained wherein the model reproduced the previous data most accurately under our experimental condition; however, the model estimated a more substantial shift than that observed in the psychophysical data. A machine learning system perceiving optical illusions will enable us to augment human perception by reducing unnecessary optical illusions and enhancing essential perceptual information.

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Cited By

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  • (2024)Deep learning models for perception of brightness related illusionsApplied Intelligence10.1007/s10489-024-05658-w54:21(10259-10283)Online publication date: 10-Aug-2024
  • (2024)A Computational Model for Color Assimilation Illusions and Color ConstancyComputer Vision – ACCV 202410.1007/978-981-96-0911-6_16(265-283)Online publication date: 8-Dec-2024
  • (2023)Apparent color picker: color prediction model to extract apparent color in photosFrontiers in Signal Processing10.3389/frsip.2023.11332103Online publication date: 9-May-2023
  • Show More Cited By
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AHs '21: Proceedings of the Augmented Humans International Conference 2021
February 2021
321 pages
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Published: 11 July 2021

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Author Tags

  1. computer simulation
  2. machine learning
  3. optical illusion
  4. perceptual augmentation

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AHs '21
AHs '21: Augmented Humans International Conference 2021
February 22 - 24, 2021
Rovaniemi, Finland

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Cited By

View all
  • (2024)Deep learning models for perception of brightness related illusionsApplied Intelligence10.1007/s10489-024-05658-w54:21(10259-10283)Online publication date: 10-Aug-2024
  • (2024)A Computational Model for Color Assimilation Illusions and Color ConstancyComputer Vision – ACCV 202410.1007/978-981-96-0911-6_16(265-283)Online publication date: 8-Dec-2024
  • (2023)Apparent color picker: color prediction model to extract apparent color in photosFrontiers in Signal Processing10.3389/frsip.2023.11332103Online publication date: 9-May-2023
  • (2023)Psychophysics may be the game-changer for deep neural networks (DNNs) to imitate the human visionBehavioral and Brain Sciences10.1017/S0140525X2300175946Online publication date: 6-Dec-2023

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