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Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Object-based color constancy in a deep neural network.

Hamed Heidari-Gorji, Karl R Gegenfurtner

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |May 3, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a deep neural network model for pixel-by-pixel color recognition under varying illumination. It assigns object reflectances, advancing computer vision color constancy beyond simple illumination estimation.

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    Area of Science:

    • Computer Vision
    • Computational Neuroscience
    • Image Processing

    Background:

    • Color constancy enables consistent color perception across different lighting conditions.
    • Traditional computer vision methods estimate illumination for image correction.
    • Human color constancy involves deeper scene and color understanding beyond illumination estimation.

    Purpose of the Study:

    • To develop a deep neural network model for assigning object reflectances.
    • To achieve pixel-by-pixel color recognition under diverse illuminations.
    • To advance computational models of human color constancy.

    Main Methods:

    • Utilized deep neural networks for reflectance assignment.
    • Employed computer graphics to generate synthetic datasets for training.
    • Developed a model for per-pixel color recognition in varied lighting.

    Main Results:

    • The model successfully assigns reflectances to objects in rendered images.
    • Demonstrated capability for pixel-by-pixel color recognition under different illuminations.
    • Showcased a novel approach to color constancy using deep learning.

    Conclusions:

    • Deep neural networks can effectively model color constancy by assigning reflectances.
    • Synthetic data generation is a viable method to overcome ground truth limitations.
    • This approach offers a more comprehensive model for computer vision color constancy.