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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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Shape-Based Measures Improve Scene Categorization.

Morteza Rezanejad, John Wilder, Dirk B Walther

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    Deep neural networks often ignore object contours, unlike humans. This study introduces algorithms to detect contour cues, significantly improving scene categorization for both humans and AI models by highlighting these underutilized visual features.

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    Area of Science:

    • Computer Vision
    • Cognitive Science
    • Artificial Intelligence

    Background:

    • Deep neural networks (DNNs) exhibit bias towards color and texture, neglecting contour information in image analysis.
    • Humans excel at object and scene recognition using contours, guided by Gestalt grouping principles.
    • Computational models lack implementations of perceptual grouping rules for mid-level vision.

    Purpose of the Study:

    • To develop novel algorithms for detecting contour-based cues in complex scenes.
    • To computationally implement Gestalt grouping rules for mid-level vision.
    • To assess the impact of contour cues on scene categorization accuracy.

    Main Methods:

    • Developed algorithms for detecting contour-based cues in complex scenes.
    • Utilized the medial axis transform (MAT) to score contours based on grouping rules.
    • Evaluated scene categorization with and without emphasized perceptual grouping information.

    Main Results:

    • Both human observers and Convolutional Neural Network (CNN) models achieved higher accuracy when perceptual grouping information was emphasized.
    • Weighting contours with the novel measures significantly boosted CNN model performance compared to unweighted contours.
    • Current CNN models do not appear to extract or utilize these contour-based grouping cues despite their importance.

    Conclusions:

    • Contour-based perceptual grouping cues are crucial for accurate scene categorization.
    • Computational implementation of Gestalt grouping rules enhances AI model performance.
    • DNNs may need architectural or training modifications to better leverage contour information.